SQL Reference

Complete reference for all SQL functions, views, and catalog tables provided by pgtrickle.

Table of Contents

• Functions
  • Core Lifecycle
    • pgtrickle.create_stream_table
    • pgtrickle.create_stream_table_if_not_exists
    • pgtrickle.create_or_replace_stream_table
    • pgtrickle.bulk_create
    • pgtrickle.alter_stream_table
    • pgtrickle.drop_stream_table
    • pgtrickle.resume_stream_table
    • pgtrickle.refresh_stream_table
    • pgtrickle.repair_stream_table
  • Status & Monitoring
    • pgtrickle.pgt_status
    • pgtrickle.health_check
    • pgtrickle.health_summary
    • pgtrickle.refresh_timeline
    • pgtrickle.st_refresh_stats
    • pgtrickle.get_refresh_history
    • pgtrickle.get_staleness
    • pgtrickle.explain_refresh_mode
    • pgtrickle.cache_stats
  • CDC Diagnostics
    • pgtrickle.slot_health
    • pgtrickle.check_cdc_health
    • pgtrickle.change_buffer_sizes
    • pgtrickle.trigger_inventory
    • pgtrickle.worker_pool_status
    • pgtrickle.parallel_job_status
    • pgtrickle.fuse_status
    • pgtrickle.reset_fuse
  • Dependency & Inspection
    • pgtrickle.dependency_tree
    • pgtrickle.diamond_groups
    • pgtrickle.pgt_scc_status
    • pgtrickle.explain_st
    • pgtrickle.list_sources
  • Utilities
    • pgtrickle.rebuild_cdc_triggers
    • pgtrickle.convert_buffers_to_unlogged
    • pgtrickle.pg_trickle_hash
    • pgtrickle.pg_trickle_hash_multi
  • Diagnostics
    • pgtrickle.recommend_refresh_mode
    • pgtrickle.refresh_efficiency
    • pgtrickle.export_definition
• Expression Support
  • Conditional Expressions
  • Comparison Operators
  • Boolean Tests
  • SQL Value Functions
  • Array and Row Expressions
  • Subquery Expressions
    • ALL (subquery) — Worked Example
  • Auto-Rewrite Pipeline
    • Window Functions in Expressions (Auto-Rewrite)
  • HAVING Clause
  • Tables Without Primary Keys (Keyless Tables)
  • Volatile Function Detection
  • COLLATE Expressions
  • IS JSON Predicate (PostgreSQL 16+)
  • SQL/JSON Constructors (PostgreSQL 16+)
  • JSON_TABLE (PostgreSQL 17+)
  • Unsupported Expression Types
• Restrictions & Interoperability
  • Referencing Other Stream Tables
  • Views as Sources in Defining Queries
  • Partitioned Tables as Sources
  • Foreign Tables as Sources
  • IMMEDIATE Mode Query Restrictions
  • Logical Replication Targets
  • Views on Stream Tables
  • Materialized Views on Stream Tables
  • Logical Replication of Stream Tables
  • Known Delta Computation Limitations
  • What Is NOT Allowed
  • Row-Level Security (RLS)
• Views
  • pgtrickle.stream_tables_info
  • pgtrickle.pg_stat_stream_tables
  • pgtrickle.quick_health
  • pgtrickle.pgt_cdc_status
• Catalog Tables
  • pgtrickle.pgt_stream_tables
  • pgtrickle.pgt_dependencies
  • pgtrickle.pgt_refresh_history
  • pgtrickle.pgt_change_tracking
  • pgtrickle.pgt_source_gates
  • pgtrickle.pgt_refresh_groups
• Delta SQL Profiling (v0.13.0)
  • pgtrickle.explain_delta
  • pgtrickle.dedup_stats
  • pgtrickle.shared_buffer_stats
• dbt Integration (v0.13.0)
  • partition_by config
  • fuse config

Functions

Core Lifecycle

Create, modify, and manage the lifecycle of stream tables.

pgtrickle.create_stream_table

Create a new stream table.

pgtrickle.create_stream_table(
    name                  text,
    query                 text,
    schedule              text      DEFAULT 'calculated',
    refresh_mode          text      DEFAULT 'AUTO',
    initialize            bool      DEFAULT true,
    diamond_consistency   text      DEFAULT NULL,
    diamond_schedule_policy text    DEFAULT NULL,
    cdc_mode              text      DEFAULT NULL,
    append_only           bool      DEFAULT false,
    pooler_compatibility_mode bool  DEFAULT false
) → void

Parameters:

When refresh_mode => 'IMMEDIATE', the cluster-wide pg_trickle.cdc_mode setting is ignored. IMMEDIATE mode always uses statement-level IVM triggers instead of CDC triggers or WAL replication slots. If you explicitly pass cdc_mode => 'wal' together with refresh_mode => 'IMMEDIATE', pg_trickle rejects the call because WAL CDC is asynchronous and incompatible with in-transaction maintenance.

Duration format:

Cron expression format:

schedule also accepts standard cron expressions for time-based scheduling. The scheduler refreshes the stream table when the cron schedule fires, rather than checking staleness.

Note: Cron-scheduled stream tables do not participate in CALCULATED schedule resolution. The stale column in monitoring views returns NULL for cron-scheduled tables.

Example:

-- Duration-based: refresh when data is staler than 2 minutes (refresh_mode defaults to 'AUTO')
SELECT pgtrickle.create_stream_table(
    name     => 'order_totals',
    query    => 'SELECT region, SUM(amount) AS total FROM orders GROUP BY region',
    schedule => '2m'
);

-- Cron-based: refresh every hour
SELECT pgtrickle.create_stream_table(
    name         => 'hourly_summary',
    query        => 'SELECT date_trunc(''hour'', ts), COUNT(*) FROM events GROUP BY 1',
    schedule     => '@hourly',
    refresh_mode => 'FULL'
);

-- Cron-based: refresh at 6 AM on weekdays
SELECT pgtrickle.create_stream_table(
    name         => 'daily_report',
    query        => 'SELECT region, SUM(revenue) AS total FROM sales GROUP BY region',
    schedule     => '0 6 * * 1-5',
    refresh_mode => 'FULL'
);

-- Immediate mode: maintained synchronously within the same transaction
-- No schedule needed — updates happen automatically when base table changes
SELECT pgtrickle.create_stream_table(
    name         => 'live_totals',
    query        => 'SELECT region, SUM(amount) AS total FROM orders GROUP BY region',
    refresh_mode => 'IMMEDIATE'
);

-- Force WAL CDC for this stream table even if the global GUC is 'trigger'
SELECT pgtrickle.create_stream_table(
    name         => 'wal_orders',
    query        => 'SELECT id, amount FROM orders',
    schedule     => '1s',
    refresh_mode => 'DIFFERENTIAL',
    cdc_mode     => 'wal'
);

Aggregate Examples:

All supported aggregate functions work in AUTO mode (and all other modes). Examples below omit refresh_mode — the default 'AUTO' selects DIFFERENTIAL automatically. Explicit modes are shown only when the mode itself is being demonstrated.

-- Algebraic aggregates (fully differential — no rescan needed)
SELECT pgtrickle.create_stream_table(
    name     => 'sales_summary',
    query    => 'SELECT region, COUNT(*) AS cnt, SUM(amount) AS total, AVG(amount) AS avg_amount
     FROM orders GROUP BY region',
    schedule => '1m'
);

-- Semi-algebraic aggregates (MIN/MAX)
SELECT pgtrickle.create_stream_table(
    name     => 'salary_ranges',
    query    => 'SELECT department, MIN(salary) AS min_sal, MAX(salary) AS max_sal
     FROM employees GROUP BY department',
    schedule => '2m'
);

-- Group-rescan aggregates (BOOL_AND/OR, STRING_AGG, ARRAY_AGG, JSON_AGG, JSONB_AGG,
--                          BIT_AND, BIT_OR, BIT_XOR, JSON_OBJECT_AGG, JSONB_OBJECT_AGG,
--                          STDDEV, STDDEV_POP, STDDEV_SAMP, VARIANCE, VAR_POP, VAR_SAMP,
--                          MODE, PERCENTILE_CONT, PERCENTILE_DISC,
--                          CORR, COVAR_POP, COVAR_SAMP, REGR_AVGX, REGR_AVGY,
--                          REGR_COUNT, REGR_INTERCEPT, REGR_R2, REGR_SLOPE,
--                          REGR_SXX, REGR_SXY, REGR_SYY, ANY_VALUE)
SELECT pgtrickle.create_stream_table(
    name     => 'team_members',
    query    => 'SELECT department,
            STRING_AGG(name, '', '' ORDER BY name) AS members,
            ARRAY_AGG(employee_id) AS member_ids,
            BOOL_AND(active) AS all_active,
            JSON_AGG(name) AS members_json
     FROM employees
     GROUP BY department',
    schedule => '1m'
);

-- Bitwise aggregates
SELECT pgtrickle.create_stream_table(
    name     => 'permission_summary',
    query    => 'SELECT department,
            BIT_OR(permissions) AS combined_perms,
            BIT_AND(permissions) AS common_perms,
            BIT_XOR(flags) AS xor_flags
     FROM employees
     GROUP BY department',
    schedule => '1m'
);

-- JSON object aggregates
SELECT pgtrickle.create_stream_table(
    name     => 'config_map',
    query    => 'SELECT department,
            JSON_OBJECT_AGG(setting_name, setting_value) AS settings,
            JSONB_OBJECT_AGG(key, value) AS metadata
     FROM config
     GROUP BY department',
    schedule => '1m'
);

-- Statistical aggregates
SELECT pgtrickle.create_stream_table(
    name     => 'salary_stats',
    query    => 'SELECT department,
            STDDEV_POP(salary) AS sd_pop,
            STDDEV_SAMP(salary) AS sd_samp,
            VAR_POP(salary) AS var_pop,
            VAR_SAMP(salary) AS var_samp
     FROM employees
     GROUP BY department',
    schedule => '1m'
);

-- Ordered-set aggregates (MODE, PERCENTILE_CONT, PERCENTILE_DISC)
SELECT pgtrickle.create_stream_table(
    name     => 'salary_percentiles',
    query    => 'SELECT department,
            MODE() WITHIN GROUP (ORDER BY grade) AS most_common_grade,
            PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY salary) AS median_salary,
            PERCENTILE_DISC(0.9) WITHIN GROUP (ORDER BY salary) AS p90_salary
     FROM employees
     GROUP BY department',
    schedule => '1m'
);

-- Regression / correlation aggregates (CORR, COVAR_*, REGR_*)
SELECT pgtrickle.create_stream_table(
    name     => 'regression_stats',
    query    => 'SELECT department,
            CORR(salary, experience) AS sal_exp_corr,
            COVAR_POP(salary, experience) AS covar_pop,
            COVAR_SAMP(salary, experience) AS covar_samp,
            REGR_SLOPE(salary, experience) AS slope,
            REGR_INTERCEPT(salary, experience) AS intercept,
            REGR_R2(salary, experience) AS r_squared,
            REGR_COUNT(salary, experience) AS regr_n
     FROM employees
     GROUP BY department',
    schedule => '1m'
);

-- ANY_VALUE aggregate (PostgreSQL 16+)
SELECT pgtrickle.create_stream_table(
    name     => 'dept_sample',
    query    => 'SELECT department, ANY_VALUE(office_location) AS sample_office
     FROM employees GROUP BY department',
    schedule => '1m'
);

-- FILTER clause on aggregates
SELECT pgtrickle.create_stream_table(
    name     => 'order_metrics',
    query    => 'SELECT region,
            COUNT(*) AS total,
            COUNT(*) FILTER (WHERE status = ''active'') AS active_count,
            SUM(amount) FILTER (WHERE status = ''shipped'') AS shipped_total
     FROM orders
     GROUP BY region',
    schedule => '1m'
);

-- PgBouncer compatibility (transaction-mode pooler)
SELECT pgtrickle.create_stream_table(
    name                      => 'pooled_orders',
    query                     => 'SELECT id, amount FROM orders',
    schedule                  => '5m',
    pooler_compatibility_mode => true
);

CTE Examples:

Non-recursive CTEs are fully supported in both FULL and DIFFERENTIAL modes:

-- Simple CTE
SELECT pgtrickle.create_stream_table(
    name     => 'active_order_totals',
    query    => 'WITH active_users AS (
        SELECT id, name FROM users WHERE active = true
    )
    SELECT a.id, a.name, SUM(o.amount) AS total
    FROM active_users a
    JOIN orders o ON o.user_id = a.id
    GROUP BY a.id, a.name',
    schedule => '1m'
);

-- Chained CTEs (CTE referencing another CTE)
SELECT pgtrickle.create_stream_table(
    name     => 'top_regions',
    query    => 'WITH regional AS (
        SELECT region, SUM(amount) AS total FROM orders GROUP BY region
    ),
    ranked AS (
        SELECT region, total FROM regional WHERE total > 1000
    )
    SELECT * FROM ranked',
    schedule => '2m'
);

-- Multi-reference CTE (referenced twice in FROM — shared delta optimization)
SELECT pgtrickle.create_stream_table(
    name     => 'self_compare',
    query    => 'WITH totals AS (
        SELECT user_id, SUM(amount) AS total FROM orders GROUP BY user_id
    )
    SELECT t1.user_id, t1.total, t2.total AS next_total
    FROM totals t1
    JOIN totals t2 ON t1.user_id = t2.user_id + 1',
    schedule => '1m'
);

-- Append-only stream table (INSERT-only fast path)
SELECT pgtrickle.create_stream_table(
    name        => 'event_log_st',
    query       => 'SELECT id, event_type, payload, created_at FROM events',
    schedule    => '30s',
    append_only => true
);

Recursive CTEs work with FULL, DIFFERENTIAL, and IMMEDIATE modes:

-- Recursive CTE (hierarchy traversal)
SELECT pgtrickle.create_stream_table(
    name         => 'category_tree',
    query        => 'WITH RECURSIVE cat_tree AS (
        SELECT id, name, parent_id, 0 AS depth
        FROM categories WHERE parent_id IS NULL
        UNION ALL
        SELECT c.id, c.name, c.parent_id, ct.depth + 1
        FROM categories c
        JOIN cat_tree ct ON c.parent_id = ct.id
    )
    SELECT * FROM cat_tree',
    schedule     => '5m',
    refresh_mode => 'FULL'  -- FULL mode: standard re-execution
);

-- Recursive CTE with DIFFERENTIAL mode (incremental semi-naive / DRed)
SELECT pgtrickle.create_stream_table(
    name         => 'org_chart',
    query        => 'WITH RECURSIVE reports AS (
        SELECT id, name, manager_id FROM employees WHERE manager_id IS NULL
        UNION ALL
        SELECT e.id, e.name, e.manager_id
        FROM employees e JOIN reports r ON e.manager_id = r.id
    )
    SELECT * FROM reports',
    schedule     => '2m',
    refresh_mode => 'DIFFERENTIAL'  -- Uses semi-naive, DRed, or recomputation (auto-selected)
);

-- Recursive CTE with IMMEDIATE mode (same-transaction maintenance)
SELECT pgtrickle.create_stream_table(
    name         => 'org_chart_live',
    query        => 'WITH RECURSIVE reports AS (
        SELECT id, name, manager_id FROM employees WHERE manager_id IS NULL
        UNION ALL
        SELECT e.id, e.name, e.manager_id
        FROM employees e JOIN reports r ON e.manager_id = r.id
    )
    SELECT * FROM reports',
    refresh_mode => 'IMMEDIATE'  -- Uses transition tables with semi-naive / DRed maintenance
);

Non-monotone recursive terms: If the recursive term contains operators like EXCEPT, aggregate functions, window functions, DISTINCT, INTERSECT (set), or anti-joins, the system automatically falls back to recomputation to guarantee correctness. Semi-naive and DRed strategies require monotone recursive terms (JOIN, UNION ALL, filter/project only).

Set Operation Examples:

INTERSECT, INTERSECT ALL, EXCEPT, EXCEPT ALL, UNION, and UNION ALL are supported:

-- INTERSECT: customers who placed orders in BOTH regions
SELECT pgtrickle.create_stream_table(
    name     => 'bi_region_customers',
    query    => 'SELECT customer_id FROM orders_east
     INTERSECT
     SELECT customer_id FROM orders_west',
    schedule => '2m'
);

-- INTERSECT ALL: preserves duplicates (bag semantics)
SELECT pgtrickle.create_stream_table(
    name     => 'common_items',
    query    => 'SELECT item_name FROM warehouse_a
     INTERSECT ALL
     SELECT item_name FROM warehouse_b',
    schedule => '1m'
);

-- EXCEPT: orders not yet shipped
SELECT pgtrickle.create_stream_table(
    name     => 'unshipped_orders',
    query    => 'SELECT order_id FROM orders
     EXCEPT
     SELECT order_id FROM shipments',
    schedule => '1m'
);

-- EXCEPT ALL: preserves duplicate counts (bag subtraction)
SELECT pgtrickle.create_stream_table(
    name     => 'excess_inventory',
    query    => 'SELECT sku FROM stock_received
     EXCEPT ALL
     SELECT sku FROM stock_shipped',
    schedule => '5m'
);

-- UNION: deduplicated merge of two sources
SELECT pgtrickle.create_stream_table(
    name     => 'all_contacts',
    query    => 'SELECT email FROM customers
     UNION
     SELECT email FROM newsletter_subscribers',
    schedule => '5m'
);

LATERAL Set-Returning Function Examples:

Set-returning functions (SRFs) in the FROM clause are supported in both FULL and DIFFERENTIAL modes. Common SRFs include jsonb_array_elements, jsonb_each, jsonb_each_text, and unnest:

-- Flatten JSONB arrays into rows
SELECT pgtrickle.create_stream_table(
    name     => 'flat_children',
    query    => 'SELECT p.id, child.value AS val
     FROM parent_data p,
     jsonb_array_elements(p.data->''children'') AS child',
    schedule => '1m'
);

-- Expand JSONB key-value pairs (multi-column SRF)
SELECT pgtrickle.create_stream_table(
    name     => 'flat_properties',
    query    => 'SELECT d.id, kv.key, kv.value
     FROM documents d,
     jsonb_each(d.metadata) AS kv',
    schedule => '2m'
);

-- Unnest arrays
SELECT pgtrickle.create_stream_table(
    name     => 'flat_tags',
    query    => 'SELECT t.id, tag.tag
     FROM tagged_items t,
     unnest(t.tags) AS tag(tag)',
    schedule => '1m'
);

-- SRF with WHERE filter
SELECT pgtrickle.create_stream_table(
    name     => 'high_value_items',
    query    => 'SELECT p.id, (e.value)::int AS amount
     FROM products p,
     jsonb_array_elements(p.prices) AS e
     WHERE (e.value)::int > 100',
    schedule => '5m'
);

-- SRF combined with aggregation
SELECT pgtrickle.create_stream_table(
    name         => 'element_counts',
    query        => 'SELECT a.id, count(*) AS cnt
     FROM arrays a,
     jsonb_array_elements(a.data) AS e
     GROUP BY a.id',
    schedule     => '1m',
    refresh_mode => 'FULL'
);

LATERAL Subquery Examples:

LATERAL subqueries in the FROM clause are supported in both FULL and DIFFERENTIAL modes. Use them for top-N per group, correlated aggregation, and conditional expansion:

-- Top-N per group: latest item per order
SELECT pgtrickle.create_stream_table(
    name     => 'latest_items',
    query    => 'SELECT o.id, o.customer, latest.amount
     FROM orders o,
     LATERAL (
         SELECT li.amount
         FROM line_items li
         WHERE li.order_id = o.id
         ORDER BY li.created_at DESC
         LIMIT 1
     ) AS latest',
    schedule => '1m'
);

-- Correlated aggregate
SELECT pgtrickle.create_stream_table(
    name     => 'dept_summaries',
    query    => 'SELECT d.id, d.name, stats.total, stats.cnt
     FROM departments d,
     LATERAL (
         SELECT SUM(e.salary) AS total, COUNT(*) AS cnt
         FROM employees e
         WHERE e.dept_id = d.id
     ) AS stats',
    schedule => '1m'
);

-- LEFT JOIN LATERAL: preserve outer rows with NULLs when subquery returns no rows
SELECT pgtrickle.create_stream_table(
    name     => 'dept_stats_all',
    query    => 'SELECT d.id, d.name, stats.total
     FROM departments d
     LEFT JOIN LATERAL (
         SELECT SUM(e.salary) AS total
         FROM employees e
         WHERE e.dept_id = d.id
     ) AS stats ON true',
    schedule => '1m'
);

WHERE Subquery Examples:

Subqueries in the WHERE clause are automatically transformed into semi-join, anti-join, or scalar subquery operators in the DVM operator tree:

-- EXISTS subquery: customers who have placed orders
SELECT pgtrickle.create_stream_table(
    name     => 'active_customers',
    query    => 'SELECT c.id, c.name
     FROM customers c
     WHERE EXISTS (SELECT 1 FROM orders o WHERE o.customer_id = c.id)',
    schedule => '1m'
);

-- NOT EXISTS: customers with no orders
SELECT pgtrickle.create_stream_table(
    name     => 'inactive_customers',
    query    => 'SELECT c.id, c.name
     FROM customers c
     WHERE NOT EXISTS (SELECT 1 FROM orders o WHERE o.customer_id = c.id)',
    schedule => '1m'
);

-- IN subquery: products that have been ordered
SELECT pgtrickle.create_stream_table(
    name     => 'ordered_products',
    query    => 'SELECT p.id, p.name
     FROM products p
     WHERE p.id IN (SELECT product_id FROM order_items)',
    schedule => '1m'
);

-- NOT IN subquery: products never ordered
SELECT pgtrickle.create_stream_table(
    name     => 'unordered_products',
    query    => 'SELECT p.id, p.name
     FROM products p
     WHERE p.id NOT IN (SELECT product_id FROM order_items)',
    schedule => '1m'
);

-- Scalar subquery in SELECT list
SELECT pgtrickle.create_stream_table(
    name     => 'products_with_max_price',
    query    => 'SELECT p.id, p.name, (SELECT max(price) FROM products) AS max_price
     FROM products p',
    schedule => '1m'
);

Notes:

• The defining query is parsed into an operator tree and validated for DVM support.
• Views as sources — views referenced in the defining query are automatically inlined as subqueries (auto-rewrite pass #0). CDC triggers are created on the underlying base tables. Nested views (view → view → table) are fully expanded. The user's original query is preserved in original_query for reinit and introspection. Materialized views are rejected in DIFFERENTIAL mode (use FULL mode or the underlying query directly). Foreign tables are also rejected in DIFFERENTIAL mode.
• CDC triggers and change buffer tables are created automatically for each source table.
• TRUNCATE on source tables — when a source table is TRUNCATEd, a CDC trigger writes a marker row (action='T') into the change buffer. On the next refresh cycle, pg_trickle detects the marker and automatically falls back to a FULL refresh. For single-source stream tables where no subsequent DML occurred after the TRUNCATE, an optimized fast path deletes all ST rows directly without re-running the full defining query.
• The ST is registered in the dependency DAG; cycles are rejected.
• Non-recursive CTEs are inlined as subqueries during parsing (Tier 1). Multi-reference CTEs share delta computation (Tier 2).
• Recursive CTEs in DIFFERENTIAL mode use three strategies, auto-selected per refresh: semi-naive evaluation for INSERT-only changes, DRed (Delete-and-Rederive) for mixed DELETE/UPDATE changes, and recomputation fallback when CTE columns do not match ST storage columns. Non-monotone recursive terms (containing EXCEPT, Aggregate, Window, DISTINCT, AntiJoin, or INTERSECT SET) automatically fall back to recomputation to ensure correctness.

Recursive CTE DIFFERENTIAL mode -- DRed algorithm (P2-1) In DIFFERENTIAL mode, mixed DELETE/UPDATE changes now use the DRed (Delete-and-Rederive) algorithm: (1) semi-naive INSERT propagation; (2) over-deletion cascade from ST storage; (3) rederivation from current source tables; (4) combine net deletions. DRed correctly handles derived-column changes such as path rebuilds under a renamed ancestor node. When CTE output columns differ from ST storage columns (mismatch), recomputation is used. Implemented in v0.10.0 (P2-1).

• LATERAL SRFs in DIFFERENTIAL mode use row-scoped recomputation: when a source row changes, only the SRF expansions for that row are re-evaluated.
• LATERAL subqueries in DIFFERENTIAL mode also use row-scoped recomputation: when an outer row changes, the correlated subquery is re-executed only for that row.
• WHERE subqueries (EXISTS, IN, scalar) are parsed into dedicated semi-join, anti-join, and scalar subquery operators with specialized delta computation.
• ALL (subquery) is the only subquery form that is currently rejected.
• ORDER BY is accepted but silently discarded — row order in the storage table is undefined (consistent with PostgreSQL's CREATE MATERIALIZED VIEW behavior). Apply ORDER BY when querying the stream table.
• TopK (ORDER BY + LIMIT) — When a top-level ORDER BY … LIMIT N is present (with a constant integer limit, optionally with OFFSET M), the query is recognized as a "TopK" pattern and accepted. TopK stream tables store exactly N rows (starting from position M+1 if OFFSET is specified) and are refreshed via a scoped-recomputation MERGE strategy. The DVM delta pipeline is bypassed; instead, each refresh re-evaluates the full ORDER BY + LIMIT [+ OFFSET] query and merges the result into the storage table. The catalog records topk_limit, topk_order_by, and optionally topk_offset for the stream table. TopK is not supported with set operations (UNION/INTERSECT/EXCEPT) or GROUP BY ROLLUP/CUBE/GROUPING SETS.
• LIMIT / OFFSET without ORDER BY are rejected — stream tables materialize the full result set. Apply LIMIT when querying the stream table.

pgtrickle.create_stream_table_if_not_exists

Create a stream table if it does not already exist. If a stream table with the given name already exists, this is a silent no-op (an INFO message is logged). The existing definition is never modified.

pgtrickle.create_stream_table_if_not_exists(
    name                    text,
    query                   text,
    schedule                text      DEFAULT 'calculated',
    refresh_mode            text      DEFAULT 'AUTO',
    initialize              bool      DEFAULT true,
    diamond_consistency     text      DEFAULT NULL,
    diamond_schedule_policy text      DEFAULT NULL,
    cdc_mode                text      DEFAULT NULL,
    append_only             bool      DEFAULT false,
    pooler_compatibility_mode bool    DEFAULT false
) → void

Parameters: Same as create_stream_table.

Example:

-- Safe to re-run in migrations:
SELECT pgtrickle.create_stream_table_if_not_exists(
    'order_totals',
    'SELECT customer_id, sum(amount) AS total FROM orders GROUP BY customer_id',
    '1m',
    'DIFFERENTIAL'
);

Notes:

• Useful for deployment / migration scripts that should be safe to re-run.
• If the stream table already exists, the provided query, schedule, and other parameters are ignored — the existing definition is preserved.

pgtrickle.create_or_replace_stream_table

Create a stream table if it does not exist, or replace the existing one if the definition changed. This is the declarative, idempotent API for deployment workflows (dbt, SQL migrations, GitOps).

pgtrickle.create_or_replace_stream_table(
    name                    text,
    query                   text,
    schedule                text      DEFAULT 'calculated',
    refresh_mode            text      DEFAULT 'AUTO',
    initialize              bool      DEFAULT true,
    diamond_consistency     text      DEFAULT NULL,
    diamond_schedule_policy text      DEFAULT NULL,
    cdc_mode                text      DEFAULT NULL,
    append_only             bool      DEFAULT false,
    pooler_compatibility_mode bool    DEFAULT false
) → void

Parameters: Same as create_stream_table.

Behavior:

The initialize parameter is honoured on create only. On replace, the stream table is always repopulated via a full refresh.

Query comparison uses the post-rewrite (normalized) form of the SQL. Cosmetic differences such as whitespace, casing, and extra parentheses are ignored.

Example:

-- Idempotent deployment — safe to run on every deploy:
SELECT pgtrickle.create_or_replace_stream_table(
    name         => 'order_totals',
    query        => 'SELECT region, SUM(amount) AS total FROM orders GROUP BY region',
    schedule     => '2m',
    refresh_mode => 'DIFFERENTIAL'
);

-- If the query changed since last deploy, the stream table is
-- migrated in place (no data gap). If nothing changed, it's a no-op.

Notes:

• Mirrors PostgreSQL's CREATE OR REPLACE convention (CREATE OR REPLACE VIEW, CREATE OR REPLACE FUNCTION).
• Never drops the stream table — even for incompatible schema changes, the ALTER QUERY path rebuilds storage in place while preserving the catalog entry (pgt_id).
• For migration scripts that should not modify an existing definition, use create_stream_table_if_not_exists instead.

pgtrickle.bulk_create

Create multiple stream tables in a single transaction.

pgtrickle.bulk_create(
    definitions  jsonb     -- Array of stream table definitions
) → jsonb                  -- Array of result objects

Each element in the definitions array must be a JSON object with at least name and query keys. All other keys match the parameters of create_stream_table (snake_case):

Returns a JSONB array of result objects:

[
  {"name": "st1", "status": "created", "pgt_id": 42},
  {"name": "st2", "status": "created", "pgt_id": 43}
]

On any error, the entire transaction is rolled back (standard PostgreSQL transactional semantics). The error message includes the index and name of the failing definition.

Example:

SELECT pgtrickle.bulk_create('[
  {"name": "order_totals", "query": "SELECT customer_id, SUM(amount) AS total FROM orders GROUP BY customer_id", "schedule": "30s"},
  {"name": "product_stats", "query": "SELECT product_id, COUNT(*) AS cnt FROM order_items GROUP BY product_id", "schedule": "1m"}
]'::jsonb);

pgtrickle.alter_stream_table

Alter properties of an existing stream table.

pgtrickle.alter_stream_table(
    name                  text,
    query                 text      DEFAULT NULL,
    schedule              text      DEFAULT NULL,
    refresh_mode          text      DEFAULT NULL,
    status                text      DEFAULT NULL,
    diamond_consistency   text      DEFAULT NULL,
    diamond_schedule_policy text    DEFAULT NULL,
    cdc_mode              text      DEFAULT NULL,
    append_only           bool      DEFAULT NULL,
    pooler_compatibility_mode bool  DEFAULT NULL,
    tier                  text      DEFAULT NULL
) → void

Parameters:

If you switch a stream table to refresh_mode => 'IMMEDIATE' while the cluster-wide pg_trickle.cdc_mode GUC is set to 'wal', pg_trickle logs an INFO and proceeds with IVM triggers. WAL CDC does not apply to IMMEDIATE mode. If the stream table has an explicit cdc_mode => 'wal' override, switching to IMMEDIATE is rejected until you change the requested CDC mode back to 'auto' or 'trigger'.

Examples:

-- Change the defining query (same output schema — fast path)
SELECT pgtrickle.alter_stream_table('order_totals',
    query => 'SELECT customer_id, SUM(amount) AS total FROM orders WHERE status = ''active'' GROUP BY customer_id');

-- Change query and add a column (compatible schema migration)
SELECT pgtrickle.alter_stream_table('order_totals',
    query => 'SELECT customer_id, SUM(amount) AS total, COUNT(*) AS cnt FROM orders GROUP BY customer_id');

-- Change query and mode simultaneously
SELECT pgtrickle.alter_stream_table('order_totals',
    query => 'SELECT customer_id, SUM(amount) AS total FROM orders GROUP BY customer_id',
    refresh_mode => 'FULL');

-- Change schedule
SELECT pgtrickle.alter_stream_table('order_totals', schedule => '5m');

-- Switch to full refresh mode
SELECT pgtrickle.alter_stream_table('order_totals', refresh_mode => 'FULL');

-- Switch to immediate (transactional) mode — installs IVM triggers, clears schedule
SELECT pgtrickle.alter_stream_table('order_totals', refresh_mode => 'IMMEDIATE');

-- Switch from immediate back to differential — re-creates CDC triggers, restores schedule
SELECT pgtrickle.alter_stream_table('order_totals',
    refresh_mode => 'DIFFERENTIAL', schedule => '5m');

-- Pin a deferred stream table to trigger CDC even when the global GUC is 'auto'
SELECT pgtrickle.alter_stream_table('order_totals', cdc_mode => 'trigger');

-- Enable append-only INSERT fast path
SELECT pgtrickle.alter_stream_table('event_log_st', append_only => true);

-- Enable pooler compatibility mode (for PgBouncer transaction mode)
SELECT pgtrickle.alter_stream_table('order_totals', pooler_compatibility_mode => true);

-- Set refresh tier (requires pg_trickle.tiered_scheduling = on)
SELECT pgtrickle.alter_stream_table('order_totals', tier => 'warm');
SELECT pgtrickle.alter_stream_table('archive_stats', tier => 'frozen');

-- Suspend a stream table
SELECT pgtrickle.alter_stream_table('order_totals', status => 'SUSPENDED');

-- Resume a suspended stream table
SELECT pgtrickle.resume_stream_table('order_totals');
-- Or via alter_stream_table
SELECT pgtrickle.alter_stream_table('order_totals', status => 'ACTIVE');

Notes:

• When query is provided, the function runs the full query rewrite pipeline (view inlining, DISTINCT ON, GROUPING SETS, etc.) and validates the new query before applying changes.
• The entire ALTER QUERY operation runs within a single transaction. If any step fails, the stream table is left unchanged.
• For same-schema and compatible-schema changes, the storage table OID is preserved — views, policies, and publications referencing the stream table remain valid.
• For incompatible schema changes (e.g., changing a column from integer to text), the storage table is rebuilt and the OID changes. A WARNING is emitted.
• The stream table is temporarily suspended during query migration to prevent concurrent scheduler refreshes.

pgtrickle.drop_stream_table

Drop a stream table, removing the storage table and all catalog entries.

pgtrickle.drop_stream_table(name text) → void

Parameters:

Example:

SELECT pgtrickle.drop_stream_table('order_totals');

Notes:

• Drops the underlying storage table with CASCADE.
• Removes all catalog entries (metadata, dependencies, refresh history).
• Cleans up CDC triggers and change buffer tables for source tables that are no longer tracked by any ST.

pgtrickle.resume_stream_table

Resume a suspended stream table, clearing its consecutive error count and re-enabling automated and manual refreshes.

pgtrickle.resume_stream_table(name text) → void

Parameters:

Example:

-- Resume a stream table that was auto-suspended due to repeated errors
SELECT pgtrickle.resume_stream_table('order_totals');

Notes:

• Errors if the ST is not in SUSPENDED state.
• Resets consecutive_errors to 0 and sets status = 'ACTIVE'.
• Emits a resumed event on the pg_trickle_alert NOTIFY channel.
• After resuming, the scheduler will include the ST in its next cycle.

pgtrickle.refresh_stream_table

Manually trigger a synchronous refresh of a stream table.

pgtrickle.refresh_stream_table(name text) → void

Parameters:

Example:

SELECT pgtrickle.refresh_stream_table('order_totals');

Notes:

• Blocked if the ST is SUSPENDED — use pgtrickle.resume_stream_table(name) first.
• Uses an advisory lock to prevent concurrent refreshes of the same ST.
• For DIFFERENTIAL mode, generates and applies a delta query. For FULL mode, truncates and reloads.
• Records the refresh in pgtrickle.pgt_refresh_history with initiated_by = 'MANUAL'.

pgtrickle.repair_stream_table

Repair a stream table by reinstalling any missing CDC triggers, validating catalog entries, and reconciling change buffer state.

pgtrickle.repair_stream_table(name text) → void

Parameters:

Example:

-- Reinstall missing CDC triggers after a point-in-time recovery
SELECT pgtrickle.repair_stream_table('order_totals');

Notes:

• Inspects all source tables in the stream table's dependency graph and reinstalls any missing or disabled CDC triggers.
• Validates that the stream table's catalog entry, storage table, and change buffer tables are consistent.
• Useful after pg_basebackup or PITR restores where triggers may not have been captured in the backup.
• Use pgtrickle.trigger_inventory() first to identify which triggers are missing.
• Safe to call on a healthy stream table — it is a no-op if everything is intact.

Status & Monitoring

Query the state of stream tables, view refresh statistics, and diagnose problems.

pgtrickle.pgt_status

Get the status of all stream tables.

pgtrickle.pgt_status() → SETOF record(
    name                text,
    status              text,
    refresh_mode        text,
    is_populated        bool,
    consecutive_errors  int,
    schedule            text,
    data_timestamp      timestamptz,
    staleness           interval
)

Example:

SELECT * FROM pgtrickle.pgt_status();

pgtrickle.health_check

Run a set of health checks against the pg_trickle installation and return one row per check.

pgtrickle.health_check() → SETOF record(
    check_name  text,   -- identifier for the check
    severity    text,   -- 'OK', 'WARN', or 'ERROR'
    detail      text    -- human-readable explanation
)

Filter to problems only:

SELECT check_name, severity, detail
FROM pgtrickle.health_check()
WHERE severity != 'OK';

Checks: scheduler_running, error_tables, stale_tables, needs_reinit, consecutive_errors, buffer_growth (> 10 000 pending rows), slot_lag (retained WAL above pg_trickle.slot_lag_warning_threshold_mb, default 100 MB), worker_pool (all worker tokens in use — parallel mode only), job_queue (> 10 jobs queued — parallel mode only).

pgtrickle.health_summary

Single-row summary of the entire pg_trickle deployment's health. Designed for monitoring dashboards that want one endpoint to poll instead of joining multiple views.

pgtrickle.health_summary() → SETOF record(
    total_stream_tables   int,
    active_count          int,
    error_count           int,
    suspended_count       int,
    stale_count           int,
    reinit_pending        int,
    max_staleness_seconds float8,    -- NULL if no stream tables
    scheduler_status      text,      -- 'ACTIVE', 'STOPPED', or 'NOT_LOADED'
    cache_hit_rate        float8     -- NULL if no cache lookups yet
)

Example:

SELECT * FROM pgtrickle.health_summary();

Tip: Use this in a Grafana single-stat panel or a Prometheus exporter to surface fleet-level health at a glance.

pgtrickle.refresh_timeline

Return recent refresh records across all stream tables in a single chronological view.

pgtrickle.refresh_timeline(
    max_rows int  DEFAULT 50
) → SETOF record(
    start_time      timestamptz,
    stream_table    text,
    action          text,
    status          text,
    rows_inserted   bigint,
    rows_deleted    bigint,
    duration_ms     float8,
    error_message   text
)

Example:

-- Most recent 20 events across all stream tables:
SELECT start_time, stream_table, action, status, round(duration_ms::numeric,1) AS ms
FROM pgtrickle.refresh_timeline(20);

-- Just failures in the last 100 events:
SELECT * FROM pgtrickle.refresh_timeline(100) WHERE status = 'ERROR';

pgtrickle.st_refresh_stats

Return per-ST refresh statistics aggregated from the refresh history.

pgtrickle.st_refresh_stats() → SETOF record(
    pgt_name                text,
    pgt_schema              text,
    status                 text,
    refresh_mode           text,
    is_populated           bool,
    total_refreshes        bigint,
    successful_refreshes   bigint,
    failed_refreshes       bigint,
    total_rows_inserted    bigint,
    total_rows_deleted     bigint,
    avg_duration_ms        float8,
    last_refresh_action    text,
    last_refresh_status    text,
    last_refresh_at        timestamptz,
    staleness_secs       float8,
    stale           bool
)

Example:

SELECT pgt_name, status, total_refreshes, avg_duration_ms, stale
FROM pgtrickle.st_refresh_stats();

pgtrickle.get_refresh_history

Return refresh history for a specific stream table.

pgtrickle.get_refresh_history(
    name      text,
    max_rows  int  DEFAULT 20
) → SETOF record(
    refresh_id       bigint,
    data_timestamp   timestamptz,
    start_time       timestamptz,
    end_time         timestamptz,
    action           text,
    status           text,
    rows_inserted    bigint,
    rows_deleted     bigint,
    duration_ms      float8,
    error_message    text
)

Example:

SELECT action, status, rows_inserted, duration_ms
FROM pgtrickle.get_refresh_history('order_totals', 5);

pgtrickle.get_staleness

Get the current staleness in seconds for a specific stream table.

pgtrickle.get_staleness(name text) → float8

Returns NULL if the ST has never been refreshed.

Example:

SELECT pgtrickle.get_staleness('order_totals');
-- Returns: 12.345  (seconds since last refresh)

pgtrickle.explain_refresh_mode

Added in v0.11.0

Explain the configured vs. effective refresh mode for a stream table, including the reason for any downgrade (e.g., AUTO choosing FULL).

pgtrickle.explain_refresh_mode(name text) → TABLE(
    configured_mode  text,
    effective_mode   text,
    downgrade_reason text
)

Columns:

Example:

SELECT * FROM pgtrickle.explain_refresh_mode('public.orders_summary');

pgtrickle.cache_stats

Return template cache statistics from shared memory.

Reports L1 (thread-local) hits, L2 (catalog table) hits, full misses (DVM re-parse), evictions (generation flushes), and the current L1 cache size for this backend.

pgtrickle.cache_stats() → SETOF record(
    l1_hits    bigint,
    l2_hits    bigint,
    misses     bigint,
    evictions  bigint,
    l1_size    integer
)

Example:

SELECT * FROM pgtrickle.cache_stats();

Note: Counters are cluster-wide (shared memory) except l1_size which is per-backend. Requires shared_preload_libraries = 'pg_trickle'; returns zeros when loaded dynamically.

CDC Diagnostics

Inspect CDC pipeline health, replication slots, change buffers, and trigger coverage.

pgtrickle.slot_health

Check replication slot health for all tracked CDC slots.

pgtrickle.slot_health() → SETOF record(
    slot_name          text,
    source_relid       bigint,
    active             bool,
    retained_wal_bytes bigint,
    wal_status         text
)

Example:

SELECT * FROM pgtrickle.slot_health();

pgtrickle.check_cdc_health

Check CDC health for all tracked source tables. Returns per-source health status including the current CDC mode, replication slot details, estimated lag, and any alerts.

The alert column uses the critical threshold configured by pg_trickle.slot_lag_critical_threshold_mb (default 1024 MB).

pgtrickle.check_cdc_health() → SETOF record(
    source_relid   bigint,
    source_table   text,
    cdc_mode       text,
    slot_name      text,
    lag_bytes      bigint,
    confirmed_lsn  text,
    alert          text
)

Columns:

Example:

SELECT * FROM pgtrickle.check_cdc_health();

pgtrickle.change_buffer_sizes

Show pending change counts and estimated on-disk sizes for all CDC-tracked source tables.

Returns one row per (stream_table, source_table) pair.

pgtrickle.change_buffer_sizes() → SETOF record(
    stream_table  text,     -- qualified stream table name
    source_table  text,     -- qualified source table name
    source_oid    bigint,
    cdc_mode      text,     -- 'trigger', 'wal', or 'transitioning'
    pending_rows  bigint,   -- rows in buffer not yet consumed
    buffer_bytes  bigint    -- estimated buffer table size in bytes
)

Example:

SELECT * FROM pgtrickle.change_buffer_sizes()
ORDER BY pending_rows DESC;

Useful for spotting a source table whose CDC buffer is growing unexpectedly (which may indicate a stalled differential refresh or a high-write source that has outpaced the schedule).

pgtrickle.worker_pool_status

Snapshot of the parallel refresh worker pool. Returns a single row.

pgtrickle.worker_pool_status() → SETOF record(
    active_workers  int,   -- workers currently executing refresh jobs
    max_workers     int,   -- cluster-wide worker budget (GUC)
    per_db_cap      int,   -- per-database dispatch cap (GUC)
    parallel_mode   text   -- current parallel_refresh_mode value
)

Example:

SELECT * FROM pgtrickle.worker_pool_status();

Returns 0 active workers when parallel_refresh_mode = 'off'.

pgtrickle.parallel_job_status

Active and recently completed scheduler jobs from the pgt_scheduler_jobs table. Shows jobs that are currently queued or running, plus jobs that finished within the last max_age_seconds (default 300).

pgtrickle.parallel_job_status(
    max_age_seconds int  DEFAULT 300
) → SETOF record(
    job_id         bigint,
    unit_key       text,        -- stable unit identifier (s:42, a:1,2, etc.)
    unit_kind      text,        -- 'singleton', 'atomic_group', 'immediate_closure'
    status         text,        -- 'QUEUED', 'RUNNING', 'SUCCEEDED', etc.
    member_count   int,
    attempt_no     int,
    scheduler_pid  int,
    worker_pid     int,         -- NULL if not yet claimed
    enqueued_at    timestamptz,
    started_at     timestamptz, -- NULL if still queued
    finished_at    timestamptz, -- NULL if not finished
    duration_ms    float8       -- NULL if not finished
)

Example — show running and recently failed jobs:

SELECT job_id, unit_key, status, duration_ms
FROM pgtrickle.parallel_job_status(60)
WHERE status NOT IN ('SUCCEEDED');

pgtrickle.trigger_inventory

List all CDC triggers that pg_trickle should have installed, and verify each one exists and is enabled in pg_catalog.

pgtrickle.trigger_inventory() → SETOF record(
    source_table  text,    -- qualified source table name
    source_oid    bigint,
    trigger_name  text,    -- expected trigger name
    trigger_type  text,    -- 'DML' or 'TRUNCATE'
    present       bool,    -- trigger exists in pg_catalog
    enabled       bool     -- trigger is not disabled
)

A present = false row means change capture is broken for that source.

Example:

-- Show only missing or disabled triggers:
SELECT source_table, trigger_type, trigger_name
FROM pgtrickle.trigger_inventory()
WHERE NOT present OR NOT enabled;

pgtrickle.fuse_status

Return the circuit-breaker (fuse) state for every stream table that has a fuse configured.

pgtrickle.fuse_status() → SETOF record(
    name           text,         -- stream table name
    fuse_mode      text,         -- 'off', 'on', or 'auto'
    fuse_state     text,         -- 'armed' or 'blown'
    fuse_ceiling   bigint,       -- change-count threshold
    fuse_sensitivity int,        -- consecutive over-ceiling cycles before blow
    blown_at       timestamptz,  -- when the fuse last blew (NULL if armed)
    blow_reason    text          -- reason the fuse blew (NULL if armed)
)

Example:

-- Check all fuse-enabled stream tables
SELECT name, fuse_mode, fuse_state, fuse_ceiling, blown_at
FROM pgtrickle.fuse_status();

-- Find blown fuses
SELECT name, blow_reason, blown_at
FROM pgtrickle.fuse_status()
WHERE fuse_state = 'blown';

Notes:

• Returns one row per stream table where fuse_mode != 'off'.
• A blown fuse suspends differential refreshes until cleared with pgtrickle.reset_fuse().
• A pgtrickle_alert NOTIFY with event fuse_blown is emitted when the fuse trips.
• See Configuration — fuse_default_ceiling for global defaults.

pgtrickle.reset_fuse

Clear a blown circuit-breaker fuse and resume scheduling for the stream table.

pgtrickle.reset_fuse(name text, action text DEFAULT 'apply') → void

Parameters:

Actions:

Example:

-- After investigating a bulk load, apply the changes:
SELECT pgtrickle.reset_fuse('category_summary', action => 'apply');

-- Or skip the oversized batch entirely:
SELECT pgtrickle.reset_fuse('category_summary', action => 'skip_changes');

-- Or rebuild from scratch:
SELECT pgtrickle.reset_fuse('category_summary', action => 'reinitialize');

Notes:

• Errors if the stream table's fuse is not in 'blown' state.
• After reset, the fuse returns to 'armed' state and the scheduler resumes normal operation.
• Use pgtrickle.fuse_status() to inspect the fuse state before resetting.
• The 'skip_changes' action advances the frontier past the pending changes without applying them — use only when you are certain the changes should be discarded.

Dependency & Inspection

Visualize dependencies, understand query plans, and audit source table relationships.

pgtrickle.dependency_tree

Render all stream table dependencies as an indented ASCII tree.

pgtrickle.dependency_tree() → SETOF record(
    tree_line    text,    -- indented visual line (├──, └──, │ characters)
    node         text,    -- qualified name (schema.table)
    node_type    text,    -- 'stream_table' or 'source_table'
    depth        int,
    status       text,    -- NULL for source_table nodes
    refresh_mode text     -- NULL for source_table nodes
)

Roots (stream tables with no stream-table parents) appear at depth 0. Each dependent is indented beneath its parent. Plain source tables are rendered as leaf nodes tagged [src].

Example:

SELECT tree_line, status, refresh_mode
FROM pgtrickle.dependency_tree();

tree_line                               status   refresh_mode
----------------------------------------+---------+--------------
report_summary                          ACTIVE   DIFFERENTIAL
├── orders_by_region                    ACTIVE   DIFFERENTIAL
│   ├── public.orders [src]
│   └── public.customers [src]
└── revenue_totals                      ACTIVE   DIFFERENTIAL
    └── public.orders [src]

pgtrickle.diamond_groups

List all detected diamond dependency groups and their members.

When stream tables form diamond-shaped dependency graphs (multiple paths converge at a single fan-in node), the scheduler groups them for coordinated refresh. This function exposes those groups for monitoring and debugging.

pgtrickle.diamond_groups() → SETOF record(
    group_id        int4,
    member_name     text,
    member_schema   text,
    is_convergence  bool,
    epoch           int8,
    schedule_policy text
)

Return columns:

Example:

SELECT * FROM pgtrickle.diamond_groups();

Notes:

• Singleton stream tables (not part of any diamond) are omitted.
• The DAG is rebuilt on each call from the catalog — results reflect the current dependency graph.
• Groups are only relevant when diamond_consistency = 'atomic' is set on the convergence node or globally via the pg_trickle.diamond_consistency GUC.

pgtrickle.pgt_scc_status

List all cyclic strongly connected components (SCCs) and their convergence status.

When stream tables form circular dependencies (with pg_trickle.allow_circular = true), they are grouped into SCCs and iterated to a fixed point. This function exposes those groups for monitoring and debugging.

pgtrickle.pgt_scc_status() → SETOF record(
    scc_id              int4,
    member_count        int4,
    members             text[],
    last_iterations     int4,
    last_converged_at   timestamptz
)

Return columns:

Example:

SELECT * FROM pgtrickle.pgt_scc_status();

Notes:

• Only cyclic SCCs (with scc_id IS NOT NULL) are returned. Acyclic stream tables are omitted.
• last_iterations reflects the maximum last_fixpoint_iterations across SCC members.
• Results are queried from the catalog on each call.

pgtrickle.explain_st

Explain the DVM plan for a stream table's defining query.

pgtrickle.explain_st(name text) → SETOF record(
    property  text,
    value     text
)

Example:

SELECT * FROM pgtrickle.explain_st('order_totals');

Output Fields

Note: Properties are only included when data is available. For example, source_partitions only appears when at least one source table is partitioned, and refresh_timing_stats only appears after at least one completed refresh.

pgtrickle.list_sources

List the source tables that a stream table depends on.

pgtrickle.list_sources(name text) → SETOF record(
    source_table   text,         -- qualified source table name
    source_oid     bigint,
    source_type    text,         -- 'table', 'stream_table', etc.
    cdc_mode       text,         -- 'trigger', 'wal', or 'transitioning'
    columns_used   text          -- column-level dependency info (if available)
)

Example:

SELECT * FROM pgtrickle.list_sources('order_totals');

Returns the tables tracked by CDC for the given stream table, along with how they are being tracked. Useful when diagnosing why a stream table is not refreshing or to audit which source tables are being trigger-tracked.

Utilities

Utility functions for CDC management and row identity hashing.

pgtrickle.rebuild_cdc_triggers

Rebuild all CDC triggers (function body + trigger DDL) for every source table tracked by pg_trickle. This recreates trigger functions and re-attaches the trigger to each source table.

pgtrickle.rebuild_cdc_triggers() → text

Returns 'done' on success. Emits a WARNING per table on error and continues processing remaining sources.

When to use:

• After changing pg_trickle.cdc_trigger_mode from row to statement (or vice versa).
• After ALTER EXTENSION pg_trickle UPDATE when the CDC trigger function body has changed.
• After restoring from a backup where triggers may have been lost.

Example:

-- Switch to statement-level triggers and rebuild
SET pg_trickle.cdc_trigger_mode = 'statement';
SELECT pgtrickle.rebuild_cdc_triggers();

Notes:

• Called automatically during ALTER EXTENSION pg_trickle UPDATE (0.3.0 → 0.4.0) migration.
• Safe to call at any time — existing triggers are dropped and recreated.
• On error for a specific table, a WARNING is logged and processing continues with remaining sources.

pgtrickle.pg_trickle_hash

Compute a 64-bit xxHash row ID from a text value.

pgtrickle.pg_trickle_hash(input text) → bigint

Marked IMMUTABLE, PARALLEL SAFE.

Example:

SELECT pgtrickle.pg_trickle_hash('some_key');
-- Returns: 1234567890123456789

pgtrickle.pg_trickle_hash_multi

Compute a row ID by hashing multiple text values (composite keys).

pgtrickle.pg_trickle_hash_multi(inputs text[]) → bigint

Marked IMMUTABLE, PARALLEL SAFE. Uses \x1E (record separator) between values and \x00NULL\x00 for NULL entries.

Example:

SELECT pgtrickle.pg_trickle_hash_multi(ARRAY['key1', 'key2']);

Operator Support Matrix — Summary

pg_trickle supports 60+ SQL constructs across three refresh modes. The table below summarises broad categories. For the complete per-operator matrix (including notes on caveats, auxiliary columns and strategies), see DVM_OPERATORS.md.

Legend: ✅ fully supported — ⚠️ supported with caveats — ❌ not supported

For details on each operator's delta strategy, auxiliary columns, and known limitations, see the full Operator Support Matrix.

Expression Support

pgtrickle's DVM parser supports a wide range of SQL expressions in defining queries. All expressions work in both FULL and DIFFERENTIAL modes.

Conditional Expressions

Comparison Operators

Boolean Tests

SQL Value Functions

Array and Row Expressions

Subquery Expressions

Subqueries are supported in the WHERE clause and SELECT list. They are parsed into dedicated DVM operators with specialized delta computation for incremental maintenance.

Notes:

• EXISTS and IN (subquery) in the WHERE clause are transformed into semi-join operators. NOT EXISTS and NOT IN (subquery) become anti-join operators.
• Multi-column IN (subquery) is not supported (e.g., WHERE (a, b) IN (SELECT x, y FROM ...)). Rewrite as WHERE EXISTS (SELECT 1 FROM ... WHERE a = x AND b = y) for equivalent semantics.
• Multiple subqueries in the same WHERE clause are supported when combined with AND. Subqueries combined with OR are also supported — they are automatically rewritten into UNION of separate filtered queries.
• Scalar subqueries in the SELECT list are supported as long as they return exactly one row and one column.
• ALL (subquery) is supported — see the worked example below.

ALL (subquery) — Worked Example

ALL (subquery) tests whether a comparison holds against every row returned by the subquery. pg_trickle rewrites it to a NULL-safe anti-join so it can be maintained incrementally.

Comparison operators supported: >, >=, <, <=, =, <>

Example — products cheaper than all competitors:

-- Source tables
CREATE TABLE products (
    id    INT PRIMARY KEY,
    name  TEXT,
    price NUMERIC
);
CREATE TABLE competitor_prices (
    id          INT PRIMARY KEY,
    product_id  INT,
    price       NUMERIC
);

-- Sample data
INSERT INTO products VALUES (1, 'Widget', 9.99), (2, 'Gadget', 24.99), (3, 'Gizmo', 14.99);
INSERT INTO competitor_prices VALUES (1, 1, 12.99), (2, 1, 11.50), (3, 2, 19.99), (4, 3, 14.99);

-- Stream table: find products priced below ALL competitor prices
SELECT pgtrickle.create_stream_table(
    name  => 'cheapest_products',
    query => $$
        SELECT p.id, p.name, p.price
        FROM products p
        WHERE p.price < ALL (
            SELECT cp.price
            FROM competitor_prices cp
            WHERE cp.product_id = p.id
        )
    $$,
    schedule => '1m'
);

Result: Widget (9.99 < all of [12.99, 11.50]) is included. Gadget (24.99 ≮ 19.99) is excluded. Gizmo (14.99 ≮ 14.99) is excluded.

How pg_trickle handles it internally:

1. WHERE price < ALL (SELECT ...) is parsed into an anti-join with a NULL-safe condition.
2. The condition NOT (x op col) is wrapped as (col IS NULL OR NOT (x op col)) to correctly handle NULL values in the subquery — if any subquery row is NULL, the ALL comparison fails (standard SQL semantics).
3. The anti-join uses the same incremental delta computation as NOT EXISTS, so changes to either products or competitor_prices are propagated efficiently.

Other common patterns:

-- Employees whose salary meets or exceeds all department maximums
WHERE salary >= ALL (SELECT max_salary FROM department_caps)

-- Orders with ratings better than all thresholds
WHERE rating > ALL (SELECT min_rating FROM quality_thresholds)

Auto-Rewrite Pipeline

pg_trickle transparently rewrites certain SQL constructs before parsing. These rewrites are applied automatically and require no user action:

Window Functions in Expressions (Auto-Rewrite)

Window functions nested inside expressions (e.g., CASE WHEN ROW_NUMBER() ..., ABS(RANK() OVER (...) - 5)) are automatically rewritten. pg_trickle lifts each window function call into a synthetic column in an inner subquery, then applies the original expression in the outer SELECT.

This rewrite is transparent — you write your query naturally and pg_trickle handles it:

Your query:

SELECT
    id,
    name,
    CASE WHEN ROW_NUMBER() OVER (PARTITION BY dept ORDER BY salary DESC) = 1
         THEN 'top earner'
         ELSE 'other'
    END AS rank_label
FROM employees

What pg_trickle generates internally:

SELECT
    "__pgt_wf_inner".id,
    "__pgt_wf_inner".name,
    CASE WHEN "__pgt_wf_inner"."__pgt_wf_1" = 1
         THEN 'top earner'
         ELSE 'other'
    END AS "rank_label"
FROM (
    SELECT *, ROW_NUMBER() OVER (PARTITION BY dept ORDER BY salary DESC) AS "__pgt_wf_1"
    FROM employees
) "__pgt_wf_inner"

The inner subquery produces the window function result as a plain column (__pgt_wf_1), which the DVM engine can maintain incrementally using its existing window function support. The outer expression is then a simple column reference.

More examples:

-- Arithmetic with window functions
SELECT id, ABS(RANK() OVER (ORDER BY score) - 5) AS adjusted_rank
FROM players

-- COALESCE with window function
SELECT id, COALESCE(LAG(value) OVER (ORDER BY ts), 0) AS prev_value
FROM sensor_readings

-- Multiple window functions in expressions
SELECT id,
       ROW_NUMBER() OVER (ORDER BY created_at) * 100 AS seq,
       SUM(amount) OVER (ORDER BY created_at) / COUNT(*) OVER (ORDER BY created_at) AS running_avg
FROM transactions

All of these are handled automatically — each distinct window function call is extracted to its own __pgt_wf_N synthetic column.

HAVING Clause

HAVING is fully supported. The filter predicate is applied on top of the aggregate delta computation — groups that pass the HAVING condition are included in the stream table.

SELECT pgtrickle.create_stream_table(
    name     => 'big_departments',
    query    => 'SELECT department, COUNT(*) AS cnt FROM employees GROUP BY department HAVING COUNT(*) > 10',
    schedule => '1m'
);

Tables Without Primary Keys (Keyless Tables)

Tables without a primary key can be used as sources. pg_trickle generates a content-based row identity by hashing all column values using pg_trickle_hash_multi(). This allows DIFFERENTIAL mode to work, though at the cost of being unable to distinguish truly duplicate rows (rows with identical values in all columns).

-- No primary key — pg_trickle uses content hashing for row identity
CREATE TABLE events (ts TIMESTAMPTZ, payload JSONB);
SELECT pgtrickle.create_stream_table(
    name     => 'event_summary',
    query    => 'SELECT payload->>''type'' AS event_type, COUNT(*) FROM events GROUP BY 1',
    schedule => '1m'
);

Known Limitation — Duplicate Rows in Keyless Tables (G7.1)

When a keyless table contains exact duplicate rows (identical values in every column), content-based hashing produces the same __pgt_row_id for each copy. Consequences:

• INSERT of a duplicate row may appear as a no-op (the hash already exists in the stream table).
• DELETE of one copy may delete all copies (the MERGE matches on __pgt_row_id, hitting every duplicate).
• Aggregate counts over keyless tables with duplicates may drift from the true query result.

Recommendation: Add a PRIMARY KEY or at least a UNIQUE constraint to source tables used in DIFFERENTIAL mode. This eliminates the ambiguity entirely. If duplicates are expected and correctness matters, use FULL refresh mode, which always recomputes from scratch.

Volatile Function Detection

pg_trickle checks all functions and operators in the defining query against pg_proc.provolatile:

• VOLATILE functions (e.g., random(), clock_timestamp(), gen_random_uuid()) are rejected in DIFFERENTIAL and IMMEDIATE modes because they produce different results on each evaluation, breaking delta correctness.
• VOLATILE operators — custom operators backed by volatile functions are also detected. The check resolves the operator’s implementation function via pg_operator.oprcode and checks its volatility in pg_proc.
• STABLE functions (e.g., now(), current_timestamp, current_setting()) produce a warning in DIFFERENTIAL and IMMEDIATE modes — they are consistent within a single refresh but may differ between refreshes.
• IMMUTABLE functions are always safe and produce no warnings.

FULL mode accepts all volatility classes since it re-evaluates the entire query each time.

Volatile Function Policy (VOL-1)

The pg_trickle.volatile_function_policy GUC controls how volatile functions are handled:

-- Allow volatile functions with a warning
SET pg_trickle.volatile_function_policy = 'warn';

-- Allow volatile functions silently
SET pg_trickle.volatile_function_policy = 'allow';

-- Restore default (reject volatile functions)
SET pg_trickle.volatile_function_policy = 'reject';

COLLATE Expressions

COLLATE clauses on expressions are supported:

SELECT pgtrickle.create_stream_table(
    name     => 'sorted_names',
    query    => 'SELECT name COLLATE "C" AS c_name FROM users',
    schedule => '1m'
);

IS JSON Predicate (PostgreSQL 16+)

The IS JSON predicate validates whether a value is valid JSON. All variants are supported:

-- Filter rows with valid JSON
SELECT pgtrickle.create_stream_table(
    name     => 'valid_json_events',
    query    => 'SELECT id, payload FROM events WHERE payload::text IS JSON',
    schedule => '1m'
);

-- Type-specific checks
SELECT pgtrickle.create_stream_table(
    name         => 'json_objects_only',
    query        => 'SELECT id, data IS JSON OBJECT AS is_obj,
          data IS JSON ARRAY AS is_arr,
          data IS JSON SCALAR AS is_scalar
   FROM json_data',
    schedule     => '1m',
    refresh_mode => 'FULL'
);

Supported variants: IS JSON, IS JSON OBJECT, IS JSON ARRAY, IS JSON SCALAR, IS NOT JSON (all forms), WITH UNIQUE KEYS.

SQL/JSON Constructors (PostgreSQL 16+)

SQL-standard JSON constructor functions are supported in both FULL and DIFFERENTIAL modes:

-- JSON_OBJECT: construct a JSON object from key-value pairs
SELECT pgtrickle.create_stream_table(
    name     => 'user_json',
    query    => 'SELECT id, JSON_OBJECT(''name'' : name, ''age'' : age) AS data FROM users',
    schedule => '1m'
);

-- JSON_ARRAY: construct a JSON array from values
SELECT pgtrickle.create_stream_table(
    name         => 'value_arrays',
    query        => 'SELECT id, JSON_ARRAY(a, b, c) AS arr FROM measurements',
    schedule     => '1m',
    refresh_mode => 'FULL'
);

-- JSON(): parse a text value as JSON
-- JSON_SCALAR(): wrap a scalar value as JSON
-- JSON_SERIALIZE(): serialize a JSON value to text

Note: JSON_ARRAYAGG() and JSON_OBJECTAGG() are SQL-standard aggregate functions fully recognized by the DVM engine. In DIFFERENTIAL mode, they use the group-rescan strategy (affected groups are re-aggregated from source data). The full deparsed SQL is preserved to handle the special key: value, ABSENT ON NULL, ORDER BY, and RETURNING clause syntax.

JSON_TABLE (PostgreSQL 17+)

JSON_TABLE() generates a relational table from JSON data. It is supported in the FROM clause in both FULL and DIFFERENTIAL modes. Internally, it is modeled as a LateralFunction.

-- Extract structured data from a JSON column
SELECT pgtrickle.create_stream_table(
    name     => 'user_phones',
    query    => $$SELECT u.id, j.phone_type, j.phone_number
    FROM users u,
         JSON_TABLE(u.contact_info, '$.phones[*]'
           COLUMNS (
             phone_type TEXT PATH '$.type',
             phone_number TEXT PATH '$.number'
           )
         ) AS j$$,
    schedule => '1m'
);

Supported column types:

• Regular columns — name TYPE PATH '$.path' (with optional ON ERROR/ON EMPTY behaviors)
• EXISTS columns — name TYPE EXISTS PATH '$.path'
• Formatted columns — name TYPE FORMAT JSON PATH '$.path'
• Nested columns — NESTED PATH '$.path' COLUMNS (...)

The PASSING clause is also supported for passing named variables to path expressions.

Unsupported Expression Types

The following are rejected with clear error messages rather than producing broken SQL:

Note: Window functions inside expressions (e.g., CASE WHEN ROW_NUMBER() OVER (...) ...) were unsupported in earlier versions but are now automatically rewritten — see Auto-Rewrite Pipeline § Window Functions in Expressions.

Restrictions & Interoperability

Stream tables are standard PostgreSQL heap tables stored in the pgtrickle schema with an additional __pgt_row_id BIGINT PRIMARY KEY column managed by the refresh engine. This section describes what you can and cannot do with them.

Referencing Other Stream Tables

Stream tables can reference other stream tables in their defining query. This creates a dependency edge in the internal DAG, and the scheduler refreshes upstream tables before downstream ones. By default, cycles are detected and rejected at creation time.

When pg_trickle.allow_circular = true, circular dependencies are allowed for stream tables that use DIFFERENTIAL refresh mode and have monotone defining queries (no aggregates, EXCEPT, window functions, or NOT EXISTS/NOT IN). Cycle members are assigned an scc_id and the scheduler iterates them to a fixed point. Non-monotone operators are rejected because they prevent convergence.

-- ST1 reads from a base table
SELECT pgtrickle.create_stream_table(
    name     => 'order_totals',
    query    => 'SELECT customer_id, SUM(amount) AS total FROM orders GROUP BY customer_id',
    schedule => '1m'
);

-- ST2 reads from ST1
SELECT pgtrickle.create_stream_table(
    name     => 'big_customers',
    query    => 'SELECT customer_id, total FROM pgtrickle.order_totals WHERE total > 1000',
    schedule => '1m'
);

Views as Sources in Defining Queries

PostgreSQL views can be used as source tables in a stream table's defining query. Views are automatically inlined — replaced with their underlying SELECT definition as subqueries — so CDC triggers land on the actual base tables.

CREATE VIEW active_orders AS
  SELECT * FROM orders WHERE status = 'active';

-- This works (views are auto-inlined):
SELECT pgtrickle.create_stream_table(
    name     => 'order_summary',
    query    => 'SELECT customer_id, COUNT(*) FROM active_orders GROUP BY customer_id',
    schedule => '1m'
);
-- Internally, 'active_orders' is replaced with:
--   (SELECT ... FROM orders WHERE status = 'active') AS active_orders

Nested views (view → view → table) are fully expanded via a fixpoint loop. Column-renaming views (CREATE VIEW v(a, b) AS ...) work correctly — pg_get_viewdef() produces the proper column aliases.

When a view is inlined, the user's original SQL is stored in the original_query catalog column for reinit and introspection. The defining_query column contains the expanded (post-inlining) form.

DDL hooks: CREATE OR REPLACE VIEW on a view that was inlined into a stream table marks that ST for reinit. DROP VIEW sets affected STs to ERROR status.

Materialized views are rejected in DIFFERENTIAL mode — their stale-snapshot semantics prevent CDC triggers from tracking changes. Use the underlying query directly, or switch to FULL mode. In FULL mode, materialized views are allowed (no CDC needed).

Foreign tables are rejected in DIFFERENTIAL mode — row-level triggers cannot be created on foreign tables. Use FULL mode instead.

Partitioned Tables as Sources

Partitioned tables are fully supported as source tables in both FULL and DIFFERENTIAL modes. CDC triggers are installed on the partitioned parent table, and PostgreSQL 13+ ensures the trigger fires for all DML routed to child partitions. The change buffer uses the parent table's OID (pgtrickle_changes.changes_<parent_oid>).

CREATE TABLE orders (
    id INT, region TEXT, amount NUMERIC
) PARTITION BY LIST (region);
CREATE TABLE orders_us PARTITION OF orders FOR VALUES IN ('US');
CREATE TABLE orders_eu PARTITION OF orders FOR VALUES IN ('EU');

-- Works — inserts into any partition are captured:
SELECT pgtrickle.create_stream_table(
    name     => 'order_summary',
    query    => 'SELECT region, SUM(amount) FROM orders GROUP BY region',
    schedule => '1m'
);

ATTACH PARTITION detection: When a new partition is attached to a tracked source table via ALTER TABLE parent ATTACH PARTITION child ..., pg_trickle's DDL event trigger detects the change in partition structure and automatically marks affected stream tables for reinitialize. This ensures pre-existing rows in the newly attached partition are included on the next refresh. DETACH PARTITION is also detected and triggers reinitialization.

WAL mode: When using WAL-based CDC (cdc_mode = 'wal'), publications for partitioned source tables are created with publish_via_partition_root = true. This ensures changes from child partitions are published under the parent table's identity, matching trigger-mode CDC behavior.

Note: pg_trickle targets PostgreSQL 18. On PostgreSQL 12 or earlier (not supported), parent triggers do not fire for partition-routed rows, which would cause silent data loss.

Foreign Tables as Sources

Foreign tables (via postgres_fdw or other FDWs) can be used as stream table sources with these constraints:

-- Foreign table source — FULL refresh only
SELECT pgtrickle.create_stream_table(
    name         => 'remote_summary',
    query        => 'SELECT region, SUM(amount) FROM remote_orders GROUP BY region',
    schedule     => '5m',
    refresh_mode => 'FULL'
);

When pg_trickle detects a foreign table source, it emits an INFO message explaining the constraints. If you attempt to use DIFFERENTIAL mode without polling enabled, the creation will succeed but the refresh falls back to FULL.

Polling-based CDC creates a local snapshot table and computes EXCEPT ALL differences on each refresh. Enable with:

SET pg_trickle.foreign_table_polling = on;

For a complete step-by-step setup guide, see the Foreign Table Sources tutorial.

IMMEDIATE Mode Query Restrictions

The 'IMMEDIATE' refresh mode supports nearly all SQL constructs supported by 'DIFFERENTIAL' and 'FULL' modes. Queries are validated at stream table creation and when switching to IMMEDIATE mode via alter_stream_table.

Supported in IMMEDIATE mode:

• Simple SELECT ... FROM table scans, filters, projections
• JOIN (INNER, LEFT, FULL OUTER)
• GROUP BY with standard aggregates (COUNT, SUM, AVG, MIN, MAX, etc.)
• DISTINCT
• Non-recursive WITH (CTEs)
• UNION ALL, INTERSECT, EXCEPT
• EXISTS / IN subqueries (SemiJoin, AntiJoin)
• Subqueries in FROM
• Window functions (ROW_NUMBER, RANK, DENSE_RANK, etc.)
• LATERAL subqueries
• LATERAL set-returning functions (unnest(), jsonb_array_elements(), etc.)
• Scalar subqueries in SELECT
• Cascading IMMEDIATE stream tables (ST depending on another IMMEDIATE ST)
• Recursive CTEs (WITH RECURSIVE) — uses semi-naive evaluation (INSERT-only) or Delete-and-Rederive (DELETE/UPDATE); bounded by pg_trickle.ivm_recursive_max_depth (default 100) to guard against infinite loops from cyclic data

Not yet supported in IMMEDIATE mode:

None — all constructs that work in 'DIFFERENTIAL' mode are now also available in 'IMMEDIATE' mode.

Notes on WITH RECURSIVE in IMMEDIATE mode:

• A __pgt_depth counter is injected into the generated semi-naive SQL. Propagation stops when the counter reaches ivm_recursive_max_depth (default 100). Raise this GUC for deeper hierarchies or set it to 0 to disable the guard.
• A WARNING is emitted at stream table creation time reminding operators to monitor for stack depth limit exceeded errors on very deep hierarchies.
• Non-linear recursion (multiple self-references) is rejected — PostgreSQL itself enforces this restriction.

Attempting to create a stream table with an unsupported construct produces a clear error message.

Logical Replication Targets

Tables that receive data via logical replication require special consideration. Changes arriving via replication do not fire normal row-level triggers, which means CDC triggers will miss those changes.

pg_trickle emits a WARNING at stream table creation time if any source table is detected as a logical replication target (via pg_subscription_rel).

Workarounds:

• Use cdc_mode = 'wal' for WAL-based CDC that captures all changes regardless of origin.
• Use FULL refresh mode, which recomputes entirely from the current table state.
• Set a frequent refresh schedule with FULL mode to limit staleness.

Views on Stream Tables

PostgreSQL views can reference stream tables. The view reflects the data as of the most recent refresh.

CREATE VIEW top_customers AS
SELECT customer_id, total
FROM pgtrickle.order_totals
WHERE total > 500
ORDER BY total DESC;

Materialized Views on Stream Tables

Materialized views can reference stream tables, though this is typically redundant (both are physical snapshots of a query). The materialized view requires its own REFRESH MATERIALIZED VIEW — it does not auto-refresh when the stream table refreshes.

Logical Replication of Stream Tables

Stream tables can be published for logical replication like any ordinary table:

-- On publisher
CREATE PUBLICATION my_pub FOR TABLE pgtrickle.order_totals;

-- On subscriber
CREATE SUBSCRIPTION my_sub
  CONNECTION 'host=... dbname=...'
  PUBLICATION my_pub;

Caveats:

• The __pgt_row_id column is replicated (it is the primary key), which is an internal implementation detail.
• The subscriber receives materialized data, not the defining query. Refreshes on the publisher propagate as normal DML via logical replication.
• Do not install pg_trickle on the subscriber and attempt to refresh the replicated table — it will have no CDC triggers or catalog entries.
• The internal change buffer tables (pgtrickle_changes.changes_<oid>) and catalog tables are not published by default; subscribers only receive the final output.

Known Delta Computation Limitations

The following edge cases produce incorrect delta results in DIFFERENTIAL mode under specific data mutation patterns. They have no effect on FULL mode.

JOIN Key Column Change + Simultaneous Right-Side Delete — Fixed (EC-01)

Resolved in v0.14.0. This limitation no longer exists — the delta query now uses a pre-change right snapshot (R₀) for DELETE deltas, ensuring stale rows are correctly removed even when the join partner is simultaneously deleted.

The fix splits Part 1 of the JOIN delta into two arms:

• Part 1a (inserts): ΔL_inserts ⋈ R₁ — uses current right state
• Part 1b (deletes): ΔL_deletes ⋈ R₀ — uses pre-change right state

R₀ is reconstructed as R_current EXCEPT ALL ΔR_inserts UNION ALL ΔR_deletes (or via NOT EXISTS anti-join for simple Scan nodes). This ensures the DELETE half always finds the old join partner, even if that partner was deleted in the same cycle.

The fix applies to INNER JOIN, LEFT JOIN, and FULL OUTER JOIN delta operators. See DVM_OPERATORS.md for implementation details.

CUBE/ROLLUP Expansion Limit

CUBE(a, b, c...n) on N columns generates $2^N$ grouping set branches (a UNION ALL of N queries). pg_trickle rejects CUBE/ROLLUP that would produce more than 64 branches to prevent runaway memory usage during query generation. Use explicit GROUPING SETS(...) instead:

-- Rejected: CUBE(a, b, c, d, e, f, g) would generate 128 branches
-- Use instead:
SELECT pgtrickle.create_stream_table(
    name     => 'multi_dim',
    query    => 'SELECT a, b, c, SUM(v) FROM t
   GROUP BY GROUPING SETS ((a, b, c), (a, b), (a), ())',
    schedule => '5m'
);

What Is NOT Allowed

Tip: The __pgt_row_id column is visible but should be ignored by consuming queries — it is an implementation detail used for delta MERGE operations.

Internal __pgt_* Auxiliary Columns

Stream tables may contain additional hidden columns whose names begin with __pgt_. These are managed exclusively by the refresh engine — they are not part of the user-visible schema and should never be read or written by application queries.

__pgt_row_id — Row identity (always present)

Every stream table has a BIGINT PRIMARY KEY column named __pgt_row_id. It is a content hash of all output columns (xxHash3-128 with Fibonacci-mixing of multiple column hashes), updated by the refresh engine on every MERGE. It is used as the MERGE join key to detect inserts/updates/deletes.

__pgt_count — Group multiplicity (aggregates & DISTINCT)

Added when the defining query contains GROUP BY, DISTINCT, UNION ALL ... GROUP BY, or any aggregate expression that requires tracking how many source rows contribute to each output row.

__pgt_count_l / __pgt_count_r — Dual multiplicity (INTERSECT / EXCEPT)

Added when the defining query contains INTERSECT or EXCEPT. Stores independently the left-branch and right-branch row counts for Z-set delta algebra.

__pgt_aux_sum_<alias> / __pgt_aux_count_<alias> — Running totals for AVG

Pairs of auxiliary columns added for each AVG(expr) in the query. Instead of recomputing the average from scratch on each delta, the refresh engine maintains a running sum and count and derives the average algebraically.

Named __pgt_aux_sum_<output_alias> and __pgt_aux_count_<output_alias>, where <output_alias> is the column alias for the AVG expression in the SELECT list.

__pgt_aux_sum2_<alias> — Sum-of-squares for STDDEV / VARIANCE

Added alongside the sum/count pair when the query contains STDDEV, STDDEV_POP, STDDEV_SAMP, VARIANCE, VAR_POP, or VAR_SAMP. Enables O(1) algebraic computation of variance from the Welford identity.

__pgt_aux_sumx_* / __pgt_aux_sumy_* / __pgt_aux_sumxy_* / __pgt_aux_sumx2_* / __pgt_aux_sumy2_* — Cross-product accumulators for regression aggregates

Five auxiliary columns per aggregate, used for O(1) algebraic maintenance of the twelve PostgreSQL regression and correlation aggregates.

The five columns are named with base prefix __pgt_aux_<kind>_<output_alias> where <kind> is sumx, sumy, sumxy, sumx2, or sumy2. The shared group count is stored in the companion __pgt_aux_count_<output_alias> column.

__pgt_aux_nonnull_<alias> — Non-NULL count for SUM + FULL OUTER JOIN

Added when the query contains SUM(expr) inside a FULL OUTER JOIN aggregate. When matched rows transition to unmatched (null-padded), standard algebraic SUM would produce 0 instead of NULL. This counter tracks how many non-NULL argument values exist in each group; when it reaches zero the SUM is definitively NULL without a full rescan.

__pgt_wf_<N> — Window function lift-out (query rewrite)

Added at query-rewrite time (before storage table creation) when the defining query contains window functions embedded inside larger expressions (e.g. CASE WHEN ROW_NUMBER() OVER (...) = 1 THEN ...). The engine lifts the window function to a synthetic inner-subquery column so the outer SELECT can reference it by alias.

__pgt_depth — Recursion depth counter (recursive CTE)

Present only inside the DVM-generated SQL for recursive CTE queries. Used to limit unbounded recursion in semi-naive evaluation. Not added as a permanent column to the storage table.

Rule of thumb: Unless you see an ALTER TABLE query mentioning one of these columns, they are transparent to consuming queries. Never SELECT __pgt_* columns in application code — their names, types, and presence may change across minor versions.

Row-Level Security (RLS)

Stream tables follow the same RLS model as PostgreSQL's built-in MATERIALIZED VIEW: the refresh always materializes the full, unfiltered result set. Access control is applied at read time via RLS policies on the stream table itself.

How It Works

Recommended Pattern: RLS on the Stream Table

-- 1. Create a stream table (materializes all rows)
SELECT pgtrickle.create_stream_table(
    name  => 'order_totals',
    query => 'SELECT tenant_id, SUM(amount) AS total FROM orders GROUP BY tenant_id'
);

-- 2. Enable RLS on the stream table
ALTER TABLE pgtrickle.order_totals ENABLE ROW LEVEL SECURITY;

-- 3. Create per-tenant policies
CREATE POLICY tenant_isolation ON pgtrickle.order_totals
    USING (tenant_id = current_setting('app.tenant_id')::INT);

-- 4. Each role sees only its own rows
SET app.tenant_id = '42';
SELECT * FROM pgtrickle.order_totals;  -- only tenant 42's rows

Note: This is identical to how you would apply RLS to a regular MATERIALIZED VIEW. One stream table serves all tenants; per-tenant filtering happens at query time with zero storage duplication.

Views

pgtrickle.stream_tables_info

Status overview with computed staleness information.

SELECT * FROM pgtrickle.stream_tables_info;

Columns include all pgtrickle.pgt_stream_tables columns plus:

pgtrickle.pg_stat_stream_tables

Comprehensive monitoring view combining catalog metadata with aggregate refresh statistics.

SELECT * FROM pgtrickle.pg_stat_stream_tables;

Key columns:

pgtrickle.quick_health

Single-row health summary for dashboards and alerting. Returns the overall health status of the pg_trickle extension at a glance.

SELECT * FROM pgtrickle.quick_health;

Status values:

• EMPTY — No stream tables exist.
• OK — All stream tables are healthy and up-to-date.
• WARNING — Some tables have errors or are stale.
• CRITICAL — At least one stream table is SUSPENDED.

pgtrickle.pgt_cdc_status

Convenience view for inspecting the CDC mode and WAL slot state of every TABLE-type source for all stream tables. Useful for monitoring in-progress TRIGGER→WAL transitions.

SELECT * FROM pgtrickle.pgt_cdc_status;

Subscribe to the pgtrickle_cdc_transition NOTIFY channel to receive real-time events when a source moves between CDC modes (payload is a JSON object with source_oid, from, and to fields).

Catalog Tables

pgtrickle.pgt_stream_tables

Core metadata for each stream table.

pgtrickle.pgt_dependencies

DAG edges — records which source tables each ST depends on, including CDC mode metadata.

pgtrickle.pgt_refresh_history

Audit log of all refresh operations.

pgtrickle.pgt_change_tracking

CDC slot tracking per source table.

pgtrickle.pgt_source_gates

Bootstrap source gate registry. One row per source table that has ever been gated. Only sources with gated = true are actively blocking scheduler refreshes.

pgtrickle.pgt_refresh_groups

User-declared Cross-Source Snapshot Consistency groups (v0.9.0). A refresh group guarantees that all member stream tables are refreshed against a snapshot taken at the same point in time, preventing partial-update visibility (e.g. orders and order_lines both reflecting the same transaction boundary).

Management API

-- Create a refresh group
SELECT pgtrickle.create_refresh_group(
    'orders_snapshot',
    ARRAY['public.orders_summary', 'public.order_lines_summary'],
    'repeatable_read'   -- or 'read_committed' (default)
);

-- List all groups:
SELECT * FROM pgtrickle.refresh_groups();

-- Remove a group:
SELECT pgtrickle.drop_refresh_group('orders_snapshot');

Validation rules:

• At least 2 member stream tables are required.
• All members must exist in pgt_stream_tables.
• No member can appear in more than one refresh group.
• Valid isolation levels: 'read_committed' (default), 'repeatable_read'.

Bootstrap Source Gating (v0.5.0)

These functions let operators pause and resume scheduler-driven refreshes for individual source tables — useful during large bulk loads or ETL windows.

pgtrickle.gate_source(source TEXT)

Mark a source table as gated. The scheduler will skip any stream table that reads from this source until ungate_source() is called.

SELECT pgtrickle.gate_source('my_schema.big_source');

Manual refresh_stream_table() calls are not affected by gates.

pgtrickle.ungate_source(source TEXT)

Clear a gate set by gate_source(). After this call the scheduler resumes normal refresh scheduling for dependent stream tables.

SELECT pgtrickle.ungate_source('my_schema.big_source');

pgtrickle.source_gates()

Table function returning the current gate status for all registered sources.

SELECT * FROM pgtrickle.source_gates();
-- source_table | schema_name | gated | gated_at | ungated_at | gated_by

Typical workflow

-- 1. Gate the source before a bulk load.
SELECT pgtrickle.gate_source('orders');

-- 2. Load historical data (scheduler sits idle for orders-based STs).
COPY orders FROM '/data/historical_orders.csv';

-- 3. Ungate — the next scheduler tick refreshes everything cleanly.
SELECT pgtrickle.ungate_source('orders');

pgtrickle.bootstrap_gate_status() (v0.6.0)

Rich introspection of bootstrap gate lifecycle. Returns the same columns as source_gates() plus computed fields for debugging.

SELECT * FROM pgtrickle.bootstrap_gate_status();
-- source_table | schema_name | gated | gated_at | ungated_at | gated_by | gate_duration | affected_stream_tables

Rows are sorted with currently-gated sources first, then alphabetically.

ETL Coordination Cookbook (v0.6.0)

Step-by-step recipes for common bulk-load patterns using source gating.

Recipe 1 — Single Source Bulk Load

Gate one source table during a large data import. The scheduler pauses refreshes for all stream tables that depend on this source.

-- 1. Gate the source before loading.
SELECT pgtrickle.gate_source('orders');

-- 2. Load the data.  The scheduler sits idle for orders-dependent STs.
COPY orders FROM '/data/orders_2026.csv' WITH (FORMAT csv, HEADER);

-- 3. Ungate.  On the next tick the scheduler refreshes everything cleanly.
SELECT pgtrickle.ungate_source('orders');

Recipe 2 — Coordinated Multi-Source Load

When multiple sources feed into a shared downstream stream table, gate them all before loading so no intermediate refreshes occur.

-- 1. Gate all sources that will be loaded.
SELECT pgtrickle.gate_source('orders');
SELECT pgtrickle.gate_source('order_lines');

-- 2. Load each source (can be parallel, any order).
COPY orders FROM '/data/orders.csv' WITH (FORMAT csv, HEADER);
COPY order_lines FROM '/data/lines.csv' WITH (FORMAT csv, HEADER);

-- 3. Ungate all sources.  The scheduler refreshes downstream STs once.
SELECT pgtrickle.ungate_source('orders');
SELECT pgtrickle.ungate_source('order_lines');

Recipe 3 — Gate + Deferred Initialization

Combine gating with initialize => false to prevent incomplete initial population when sources are loaded asynchronously.

-- 1. Gate sources before creating any stream tables.
SELECT pgtrickle.gate_source('orders');
SELECT pgtrickle.gate_source('order_lines');

-- 2. Create stream tables without initial population.
SELECT pgtrickle.create_stream_table(
    'order_summary',
    'SELECT region, SUM(amount) FROM orders GROUP BY region',
    '1m', initialize => false
);
SELECT pgtrickle.create_stream_table(
    'order_report',
    'SELECT s.region, s.total, l.line_count
     FROM order_summary s
     JOIN (SELECT region, COUNT(*) AS line_count FROM order_lines GROUP BY region) l
       USING (region)',
    '1m', initialize => false
);

-- 3. Run ETL processes (can be in separate transactions).
BEGIN;
  COPY orders FROM 's3://warehouse/orders.parquet';
  SELECT pgtrickle.ungate_source('orders');
COMMIT;

BEGIN;
  COPY order_lines FROM 's3://warehouse/lines.parquet';
  SELECT pgtrickle.ungate_source('order_lines');
COMMIT;

-- 4. Once all sources are ungated, the scheduler initializes and refreshes
--    all stream tables in dependency order.

Recipe 4 — Nightly Batch Pattern

For scheduled ETL that runs overnight, gate sources before the batch starts and ungate after the batch completes.

-- Nightly ETL script:

-- Gate all sources that will be refreshed.
SELECT pgtrickle.gate_source('sales');
SELECT pgtrickle.gate_source('inventory');

-- Truncate and reload (or use COPY, INSERT...SELECT, etc.).
TRUNCATE sales;
COPY sales FROM '/data/nightly/sales.csv' WITH (FORMAT csv, HEADER);

TRUNCATE inventory;
COPY inventory FROM '/data/nightly/inventory.csv' WITH (FORMAT csv, HEADER);

-- All data loaded — ungate and let the scheduler handle the rest.
SELECT pgtrickle.ungate_source('sales');
SELECT pgtrickle.ungate_source('inventory');

-- Verify: check the gate status to confirm everything is ungated.
SELECT * FROM pgtrickle.bootstrap_gate_status();

Recipe 5 — Monitoring During a Gated Load

Use bootstrap_gate_status() to monitor progress when streams appear stalled.

-- Check which sources are currently gated and how long they've been paused.
SELECT source_table, gate_duration, affected_stream_tables
FROM pgtrickle.bootstrap_gate_status()
WHERE gated = true;

-- If a gate has been active too long (e.g. ETL failed), ungate manually.
SELECT pgtrickle.ungate_source('stale_source');

Watermark Gating (v0.7.0)

Watermark gating is a scheduling control for ETL pipelines where multiple source tables are populated by separate jobs that finish at different times. Each ETL job declares "I'm done up to timestamp X", and the scheduler waits until all sources in a group are caught up within a configurable tolerance before refreshing downstream stream tables.

Catalog Tables

pgtrickle.pgt_watermarks

Per-source watermark state. One row per source table that has had a watermark advanced.

pgtrickle.pgt_watermark_groups

Watermark group definitions. Each group declares that a set of sources must be temporally aligned.

pgtrickle.pgt_template_cache

Added in v0.16.0. Cross-backend delta SQL template cache (UNLOGGED). Stores compiled delta query templates so new backends skip the ~45 ms DVM parse+differentiate step. Managed automatically — no user interaction required.

Functions

pgtrickle.advance_watermark(source TEXT, watermark TIMESTAMPTZ)

Signal that a source table's data is complete through the given timestamp.

• Monotonic: rejects watermarks that go backward (raises error).
• Idempotent: advancing to the same value is a silent no-op.
• Transactional: the watermark is part of the caller's transaction.

SELECT pgtrickle.advance_watermark('orders', '2026-03-01 12:05:00+00');

pgtrickle.create_watermark_group(group_name TEXT, sources TEXT[], tolerance_secs FLOAT8 DEFAULT 0)

Create a watermark group. Requires at least 2 sources.

• tolerance_secs: maximum allowed lag between the most-advanced and least-advanced watermarks. Default 0 means strict alignment.

SELECT pgtrickle.create_watermark_group(
    'order_pipeline',
    ARRAY['orders', 'order_lines'],
    0    -- strict alignment (default)
);

pgtrickle.drop_watermark_group(group_name TEXT)

Remove a watermark group by name.

SELECT pgtrickle.drop_watermark_group('order_pipeline');

pgtrickle.watermarks()

Return the current watermark state for all registered sources.

SELECT * FROM pgtrickle.watermarks();

pgtrickle.watermark_groups()

Return all watermark group definitions.

SELECT * FROM pgtrickle.watermark_groups();

pgtrickle.watermark_status()

Return live alignment status for each watermark group.

SELECT * FROM pgtrickle.watermark_status();

Recipes

Recipe 6 — Nightly ETL with Watermarks

-- Create a watermark group for the order pipeline.
SELECT pgtrickle.create_watermark_group(
    'order_pipeline',
    ARRAY['orders', 'order_lines']
);

-- Nightly ETL job 1: Load orders
BEGIN;
  COPY orders FROM '/data/orders_20260301.csv';
  SELECT pgtrickle.advance_watermark('orders', '2026-03-01');
COMMIT;

-- Nightly ETL job 2: Load order lines (may run later)
BEGIN;
  COPY order_lines FROM '/data/lines_20260301.csv';
  SELECT pgtrickle.advance_watermark('order_lines', '2026-03-01');
COMMIT;

-- order_report refreshes on the next tick after both watermarks align.

Recipe 7 — Micro-Batch Tolerance

-- Allow up to 30 seconds of skew between trades and quotes.
SELECT pgtrickle.create_watermark_group(
    'realtime_pipeline',
    ARRAY['trades', 'quotes'],
    30   -- 30-second tolerance
);

-- External process advances watermarks every few seconds.
SELECT pgtrickle.advance_watermark('trades', '2026-03-01 12:00:05+00');
SELECT pgtrickle.advance_watermark('quotes', '2026-03-01 12:00:02+00');
-- Lag is 3s, within 30s tolerance → stream tables refresh normally.

Recipe 8 — Monitoring Watermark Alignment

-- Check which groups are currently misaligned.
SELECT group_name, lag_secs, aligned
FROM pgtrickle.watermark_status()
WHERE NOT aligned;

-- Check individual source watermarks.
SELECT source_table, watermark, updated_at
FROM pgtrickle.watermarks()
ORDER BY watermark;

Stuck Watermark Detection (WM-7, v0.15.0)

When pg_trickle.watermark_holdback_timeout is set to a positive value (seconds), the scheduler periodically checks all watermark sources. If any source in a watermark group has not been advanced within the timeout, downstream stream tables in that group are paused (refresh is skipped) and a pgtrickle_alert NOTIFY is emitted.

This protects against silent data staleness when an ETL pipeline breaks and stops advancing watermarks -- without this guard, stream tables would continue refreshing with stale external data.

Behavior:

• Stuck detection: Every ~60 seconds, the scheduler checks updated_at for all watermark sources. If now() - updated_at > watermark_holdback_timeout, the source is stuck.
• Pause: Any stream table whose source set overlaps a group containing a stuck source is skipped. A SKIP record with "stuck" in the reason is logged to pgt_refresh_history.
• Alert: A pgtrickle_alert NOTIFY with event watermark_stuck is emitted (once per newly-stuck source, not repeated every check cycle).
• Auto-resume: When the stuck watermark is advanced via advance_watermark(), the next scheduler check detects the advancement, lifts the pause, and emits a watermark_resumed event.

Recipe 9 — Stuck Watermark Protection

-- Enable stuck-watermark detection with a 10-minute timeout.
ALTER SYSTEM SET pg_trickle.watermark_holdback_timeout = 600;
SELECT pg_reload_conf();

-- Listen for alerts in a monitoring process.
LISTEN pgtrickle_alert;

-- When the ETL pipeline breaks and stops calling advance_watermark(),
-- the scheduler will start skipping downstream STs after 10 minutes.
-- You'll receive a NOTIFY payload like:
--   {"event":"watermark_stuck","group":"order_pipeline","source_oid":16385,"age_secs":620}

-- When the ETL pipeline recovers and advances the watermark:
SELECT pgtrickle.advance_watermark('orders', '2026-03-02 00:00:00+00');
-- The scheduler automatically resumes, and you'll receive:
--   {"event":"watermark_resumed","source_oid":16385}

Developer Diagnostics (v0.12.0)

Four SQL-callable introspection functions that surface internal DVM state without side-effects. All functions are read-only — they never modify catalog tables or trigger refreshes.

pgtrickle.explain_query_rewrite(query TEXT)

Walk a query through the full DVM rewrite pipeline and report each pass.

Returns one row per rewrite pass. When a pass changes the query, changed = true and sql_after contains the SQL after the transformation. Two synthetic rows are appended: topk_detection (detects ORDER BY … LIMIT) and dvm_patterns (lists detected DVM constructs such as aggregation strategy, join types, and volatility).

SELECT pass_name, changed, sql_after
FROM pgtrickle.explain_query_rewrite(
  'SELECT customer_id, SUM(amount) FROM orders GROUP BY customer_id'
);

Return columns:

Rewrite passes (in order):

pgtrickle.diagnose_errors(name TEXT)

Return the last 5 FAILED refresh events for a stream table, with each error classified by type and supplied with a remediation hint.

SELECT event_time, error_type, error_message, remediation
FROM pgtrickle.diagnose_errors('my_stream_table');

Return columns:

Error types:

pgtrickle.list_auxiliary_columns(name TEXT)

List all __pgt_* internal columns on a stream table's storage relation, with an explanation of each column's role.

These columns are normally hidden from SELECT * output. This function surfaces them for debugging and operator visibility.

SELECT column_name, data_type, purpose
FROM pgtrickle.list_auxiliary_columns('my_stream_table');

Return columns:

Common auxiliary columns:

pgtrickle.validate_query(query TEXT)

Parse and validate a query through the DVM pipeline without creating a stream table. Returns detected SQL constructs, warnings, and the resolved refresh mode.

SELECT check_name, result, severity
FROM pgtrickle.validate_query(
  'SELECT customer_id, COUNT(*) FROM orders GROUP BY customer_id'
);

Return columns:

The first row always has check_name = 'resolved_refresh_mode' with the mode that would be assigned under refresh_mode = 'AUTO': DIFFERENTIAL, FULL, or TOPK.

Common check names:

Example output for a GROUP_RESCAN query:

SELECT check_name, result, severity
FROM pgtrickle.validate_query(
  'SELECT grp, STRING_AGG(tag, '','') FROM events GROUP BY grp'
);

Note on GROUP_RESCAN: STRING_AGG, ARRAY_AGG, BOOL_AND, and other non-algebraic aggregates use a group-rescan strategy — any change in a group triggers full re-aggregation from the source data for that group. This is still DIFFERENTIAL (only changed groups are rescanned), but has higher per-group cost than algebraic strategies. If this is performance-sensitive, consider pre-aggregating with a simpler aggregate and post-processing.

Delta SQL Profiling (v0.13.0)

pgtrickle.explain_delta(st_name text, format text DEFAULT 'text')

Generate the delta SQL query plan for a stream table without executing a refresh.

explain_delta produces the differential delta SQL that would be used on the next DIFFERENTIAL refresh, then runs EXPLAIN (ANALYZE false, FORMAT <format>) on it and returns the plan lines. This function is useful for:

• Identifying slow joins or missing indexes in auto-generated delta SQL.
• Comparing plan complexity between different query forms.
• Monitoring how the size of change buffers affects plan shape.

The delta SQL is generated against a hypothetical "scan all changes" window (LSN 0/0 → FF/FFFFFFFF) so the plan shows the full join/filter structure even when the change buffer is currently empty.

Parameters:

Returns: SETOF text — one row per plan line (text format) or one row containing the full JSON/XML/YAML plan.

Example:

-- Show the text plan for the delta query
SELECT line FROM pgtrickle.explain_delta('public.orders_summary');

-- Get the JSON plan for programmatic analysis
SELECT line FROM pgtrickle.explain_delta('public.orders_summary', 'json');

Environment variable (PGS_PROFILE_DELTA=1): When the environment variable PGS_PROFILE_DELTA=1 is set in the PostgreSQL server process, every DIFFERENTIAL refresh automatically captures EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) for the resolved delta SQL and writes the plan to /tmp/delta_plans/<schema>_<table>.json. This is intended for E2E test diagnostics and local profiling sessions.

pgtrickle.dedup_stats()

Show MERGE deduplication profiling counters accumulated since server start.

When the delta cannot be guaranteed to contain at most one row per __pgt_row_id (e.g. for aggregate queries or keyless sources), the MERGE must group and aggregate the delta before merging. This is tracked as dedup needed. A consistently high ratio indicates that pre-MERGE compaction in the change buffer would reduce refresh latency.

Returns: one row with:

Example:

SELECT * FROM pgtrickle.dedup_stats();
-- total_diff_refreshes | dedup_needed | dedup_ratio_pct
-- ----------------------+--------------+-----------------
--                  1234 |           87 |            7.05

A dedup_ratio_pct ≥ 10 is the threshold recommended for investigating a two-pass MERGE strategy. See plans/performance/REPORT_OVERALL_STATUS.md §14 for background.

pgtrickle.shared_buffer_stats()

Added in v0.13.0

D-4 observability function. Returns one row per shared change buffer (one per tracked source table), showing how many stream tables share the buffer, which columns are tracked, the safe cleanup frontier, and the current buffer size.

Return columns:

Example:

SELECT * FROM pgtrickle.shared_buffer_stats();
-- source_oid | source_table       | consumer_count | consumers                          | columns_tracked | safe_frontier_lsn | buffer_rows | is_partitioned
-- -----------+--------------------+----------------+------------------------------------+-----------------+-------------------+-------------+----------------
--      16456 | public.orders      |              3 | public.orders_by_region, public... |               5 | 0/1A2B3C4D        |         142 | f

UNLOGGED Change Buffers (v0.14.0)

pgtrickle.convert_buffers_to_unlogged()

Converts all existing logged change buffer tables to UNLOGGED. This eliminates WAL writes for trigger-inserted CDC rows, reducing WAL amplification by ~30%.

Returns: bigint — the number of buffer tables converted.

SELECT pgtrickle.convert_buffers_to_unlogged();
-- convert_buffers_to_unlogged
-- ----------------------------
--                            5

Warning: Each conversion acquires ACCESS EXCLUSIVE lock on the buffer table. Run this function during a low-traffic maintenance window to minimize lock contention.

After conversion: Buffer contents will be lost on crash recovery. The scheduler automatically detects this and enqueues a FULL refresh for affected stream tables. See pg_trickle.unlogged_buffers for the full trade-off discussion.

Refresh Mode Diagnostics (v0.14.0)

pgtrickle.recommend_refresh_mode(st_name TEXT DEFAULT NULL)

Analyze stream table workload characteristics and recommend the optimal refresh mode (FULL vs DIFFERENTIAL). When st_name is NULL, returns one row per stream table. When provided, returns a single row for the named stream table.

The function evaluates seven weighted signals — change ratio, empirical timing, query complexity, target size, index coverage, and latency variance — and computes a composite score. Scores above +0.15 recommend DIFFERENTIAL; below −0.15 recommend FULL; in between, the function recommends KEEP (current mode is near-optimal).

Parameters:

Return columns:

Example:

-- Check all stream tables
SELECT pgt_name, current_mode, recommended_mode, confidence, reason
FROM pgtrickle.recommend_refresh_mode();

-- Check a specific stream table
SELECT recommended_mode, confidence, reason, signals
FROM pgtrickle.recommend_refresh_mode('public.orders_summary');

Signal weights:

pgtrickle.refresh_efficiency()

Per-table refresh efficiency metrics. Returns operational statistics for every stream table — useful for monitoring dashboards and Grafana alerts.

Return columns:

Example:

SELECT pgt_name, refresh_mode, diff_count, full_count,
       avg_diff_ms, avg_full_ms, diff_speedup
FROM pgtrickle.refresh_efficiency()
ORDER BY total_refreshes DESC;

Export API (v0.14.0)

pgtrickle.export_definition(st_name TEXT)

Export a stream table's configuration as reproducible DDL. Returns a SQL script containing DROP STREAM TABLE IF EXISTS followed by SELECT pgtrickle.create_stream_table(...) with all configured options, plus any ALTER STREAM TABLE calls for post-creation settings (tier, fuse mode, etc.).

Parameters:

Returns: text — SQL script that recreates the stream table.

Example:

-- Export a single definition
SELECT pgtrickle.export_definition('public.orders_summary');

-- Export all definitions
SELECT pgtrickle.export_definition(pgt_schema || '.' || pgt_name)
FROM pgtrickle.pgt_stream_tables;

dbt Integration (v0.13.0)

The dbt-pgtrickle package exposes two new config(...) keys added in v0.13.0: partition_by and the fuse circuit-breaker options. Use them directly in any stream_table materialization model.

For full dbt documentation see dbt-pgtrickle/README.md.

partition_by config

Partition the stream table's underlying storage table using PostgreSQL PARTITION BY RANGE. Only applied at creation time — changing it after the stream table exists has no effect (use --full-refresh to recreate).

-- models/marts/events_by_day.sql
{{ config(
    materialized='stream_table',
    schedule='1m',
    refresh_mode='DIFFERENTIAL',
    partition_by='event_day'
) }}

SELECT
    event_day,
    user_id,
    COUNT(*) AS event_count
FROM {{ source('raw', 'events') }}
GROUP BY event_day, user_id

pg_trickle creates a PARTITION BY RANGE (event_day) storage table with an automatic default catch-all partition. Add named partitions via standard DDL:

CREATE TABLE analytics.events_by_day_2026
  PARTITION OF analytics.events_by_day
  FOR VALUES FROM ('2026-01-01') TO ('2027-01-01');

The partition_by value is stored in pgtrickle.pgt_stream_tables.st_partition_key and visible via pgtrickle.stream_tables_info.

fuse config

The fuse circuit breaker suspends differential refreshes when the incoming change volume exceeds a threshold, preventing runaway refresh cycles during bulk ingestion. Fuse parameters are applied via alter_stream_table() on every dbt run; they are a no-op if the values have not changed.

-- models/marts/order_totals.sql
{{ config(
    materialized='stream_table',
    schedule='5m',
    refresh_mode='DIFFERENTIAL',
    fuse='auto',
    fuse_ceiling=50000,
    fuse_sensitivity=3
) }}

SELECT customer_id, SUM(amount) AS total
FROM {{ source('raw', 'orders') }}
GROUP BY customer_id

Monitor fuse state via pgtrickle.dedup_stats() or check pgtrickle.pgt_stream_tables.fuse_state directly:

SELECT pgt_name, fuse_mode, fuse_state, fuse_ceiling, fuse_sensitivity
FROM pgtrickle.pgt_stream_tables
WHERE fuse_mode != 'off';

Dog Feeding — Self-Monitoring (v0.20.0)

Added in v0.20.0.

pg_trickle can monitor itself using its own stream tables. Five dog-feeding stream tables maintain reactive analytics over the internal catalog, replacing repeated full-scan diagnostic queries with continuously-maintained incremental views.

Quick Start

-- Create all five dog-feeding stream tables (idempotent).
SELECT pgtrickle.setup_dog_feeding();

-- Check status.
SELECT * FROM pgtrickle.dog_feeding_status();

-- View threshold recommendations (after 10+ refresh cycles).
SELECT * FROM pgtrickle.df_threshold_advice
WHERE confidence IN ('HIGH', 'MEDIUM');

-- View anomalies.
SELECT * FROM pgtrickle.df_anomaly_signals
WHERE duration_anomaly IS NOT NULL;

-- Enable auto-apply (optional).
SET pg_trickle.dog_feeding_auto_apply = 'threshold_only';

-- Clean up.
SELECT pgtrickle.teardown_dog_feeding();

pgtrickle.setup_dog_feeding()

Creates all five dog-feeding stream tables. Idempotent — safe to call multiple times. Emits a warm-up warning if pgt_refresh_history has fewer than 50 rows.

Stream tables created:

pgtrickle.teardown_dog_feeding()

Drops all dog-feeding stream tables. Safe with partial setups — missing tables are silently skipped. User stream tables are never affected.

pgtrickle.dog_feeding_status()

Returns the status of all five expected dog-feeding stream tables:

pgtrickle.scheduler_overhead()

Returns scheduler efficiency metrics for the last hour:

pgtrickle.explain_dag(format)

Returns the full refresh DAG as a Mermaid markdown (default) or Graphviz DOT string. Node colours: user STs = blue, dog-feeding STs = green, suspended = red, fused = orange.

-- Mermaid format (default).
SELECT pgtrickle.explain_dag();

-- Graphviz DOT format.
SELECT pgtrickle.explain_dag('dot');

Auto-Apply Policy

The pg_trickle.dog_feeding_auto_apply GUC controls whether analytics can automatically adjust stream table configuration:

Auto-apply is rate-limited to at most one threshold change per stream table per 10 minutes. Changes are logged to pgt_refresh_history with initiated_by = 'DOG_FEED'.

Confidence Levels and Sparse History

df_threshold_advice assigns a confidence level to each recommendation:

When you see LOW confidence: This is normal during the first minutes after setup_dog_feeding(). The stream tables need time to accumulate refresh history. In typical deployments with a 1-minute schedule, expect:

• LOW for the first ~10 minutes
• MEDIUM after ~10 minutes
• HIGH after ~20 minutes (requires at least 2 FULL refreshes — these happen naturally when the auto-threshold triggers a mode switch)

If a stream table uses FULL mode exclusively, the advice will remain at MEDIUM because no DIFFERENTIAL observations exist for comparison.

The sla_headroom_pct column shows how much faster DIFFERENTIAL is compared to FULL as a percentage. A value of 70% means "DIFF is 70% faster than FULL". This column is NULL when either FULL or DIFF observations are missing.

Public API Stability Contract

Added in v0.19.0 (DB-6).

Stable (will not break without a major version bump)

Unstable (may change in any release)

Versioning Policy

• Patch releases (0.x.Y): Bug fixes only. No breaking changes.
• Minor releases (0.X.0): New features. Stable API preserved; unstable surfaces may change. Breaking changes to stable API only with a deprecation cycle (WARNING for one release, removal in the next).
• Major release (1.0.0): Stable API locked. Breaking changes require a major version bump.