Getting Started with pg_trickle

What is pg_trickle?

pg_trickle adds stream tables to PostgreSQL — tables that are defined by a SQL query and kept automatically up to date as the underlying data changes. Think of them as materialized views that refresh themselves, but smarter: instead of re-running the entire query on every refresh, pg_trickle uses Incremental View Maintenance (IVM) to process only the rows that changed.

Traditional materialized views force a choice: either re-run the full query (expensive) or accept stale data. pg_trickle eliminates this trade-off. When you insert a single row into a million-row table, pg_trickle computes the effect of that one row on the query result — it doesn’t touch the other 999,999.

How data flows

The key concept is that data flows downstream automatically — from your base tables through any chain of stream tables, without you writing a single line of orchestration code:

  You write to base tables
         │
         ▼
  ┌─────────────┐   triggers (or WAL)   ┌─────────────────────┐
  │ Base Tables │ ─────────────────────▶ │   Change Buffers    │
  │ (you write) │                        │ (pgtrickle_changes.*) │
  └─────────────┘                        └──────────┬──────────┘
                                                     │
                                           delta query (ΔQ) on refresh
                                                     │
                                                     ▼
  ┌──────────────────────────────────────────────────────────────┐
  │  Stream Table A  ◀── depends on base tables                  │
  └──────────────────────────┬───────────────────────────────────┘
                             │  change captured, buffer written
                             ▼
  ┌──────────────────────────────────────────────────────────────┐
  │  Stream Table B  ◀── depends on Stream Table A               │
  └──────────────────────────────────────────────────────────────┘

One write to a base table can ripple through an entire DAG of stream tables — each layer refreshed in the correct topological order, each doing only the work proportional to what actually changed.

  1. You write to your base tables normally — INSERT, UPDATE, DELETE
  2. Lightweight AFTER row-level triggers capture each change into a buffer, atomically in the same transaction. No polling, no logical replication slots required by default.
  3. On each refresh cycle, pg_trickle derives a delta query (ΔQ) that reads only the buffered changes since the last refresh frontier
  4. The delta is merged into the stream table — only the affected rows are written
  5. If other stream tables depend on this one, they are scheduled next (topological order)
  6. Optionally: once wal_level = logical is available and the first refresh succeeds, pg_trickle automatically transitions from triggers to WAL-based CDC (near-zero write-path overhead compared to ~2–15 μs for triggers). The transition is seamless and transparent.

This tutorial walks through a concrete org-chart example so you can see this flow end to end, including a chain of stream tables that propagates changes automatically.

What you’ll build

An employee org-chart system with two stream tables:

  • department_tree — a recursive CTE that flattens a department hierarchy into paths like Company > Engineering > Backend
  • department_stats — a join + aggregation over department_tree (a stream table!) that computes headcount and salary budget, with the full path included
  • department_report — a further aggregation that rolls up stats to top-level departments

The chain departments → department_tree → department_stats → department_report demonstrates automatic downstream propagation: modify a department name in the base table and all three stream tables update automatically, in the right order, without any manual orchestration.

By the end you will have:

  • Seen how stream tables are created, queried, and refreshed
  • Watched a single UPDATE in a base table cascade through three layers of stream tables automatically
  • Understood the three refresh modes and IVM strategies

Prefer dbt? A runnable dbt companion project mirrors every step below. Clone the repo and run: bash ./examples/dbt_getting_started/scripts/run_example.sh See examples/dbt_getting_started/ for full details.

Prerequisites

  • PostgreSQL 18.x with pg_trickle installed (see INSTALL.md)
  • shared_preload_libraries = 'pg_trickle' in postgresql.conf
  • psql or any SQL client

Connect to the database you want to use and enable the extension:

CREATE EXTENSION pg_trickle;

No additional configuration is needed. pg_trickle automatically discovers all databases on the server and starts a scheduler for each one where the extension is installed.

Step 1: Create the Base Tables

These are ordinary PostgreSQL tables — pg_trickle doesn’t require any special column types, annotations, or schema conventions.

Tables without a primary key work, but pg_trickle will emit a WARNING at stream table creation time: change detection falls back to a content-based hash across all columns, which is slower for wide tables and cannot distinguish between identical duplicate rows. Adding a primary key gives the best performance and most reliable change detection. A primary key is also required for automatic transition to WAL-based CDC (cdc_mode = 'auto'); without one the source table stays on trigger-based CDC.

-- Department hierarchy (self-referencing tree)
CREATE TABLE departments (
    id         SERIAL PRIMARY KEY,
    name       TEXT NOT NULL,
    parent_id  INT REFERENCES departments(id)
);

-- Employees belong to a department
CREATE TABLE employees (
    id            SERIAL PRIMARY KEY,
    name          TEXT NOT NULL,
    department_id INT NOT NULL REFERENCES departments(id),
    salary        NUMERIC(10,2) NOT NULL
);

Now insert some data — a three-level department tree and a handful of employees:

-- Top-level
INSERT INTO departments (id, name, parent_id) VALUES
    (1, 'Company',     NULL);

-- Second level
INSERT INTO departments (id, name, parent_id) VALUES
    (2, 'Engineering', 1),
    (3, 'Sales',       1),
    (4, 'Operations',  1);

-- Third level (under Engineering)
INSERT INTO departments (id, name, parent_id) VALUES
    (5, 'Backend',     2),
    (6, 'Frontend',    2),
    (7, 'Platform',    2);

-- Employees
INSERT INTO employees (name, department_id, salary) VALUES
    ('Alice',   5, 120000),   -- Backend
    ('Bob',     5, 115000),   -- Backend
    ('Charlie', 6, 110000),   -- Frontend
    ('Diana',   7, 130000),   -- Platform
    ('Eve',     3, 95000),    -- Sales
    ('Frank',   3, 90000),    -- Sales
    ('Grace',   4, 100000);   -- Operations

At this point these are plain tables with no triggers, no change tracking, nothing special. The department tree looks like this:

Company (1)
├── Engineering (2)
│   ├── Backend (5)     — Alice, Bob
│   ├── Frontend (6)    — Charlie
│   └── Platform (7)    — Diana
├── Sales (3)           — Eve, Frank
└── Operations (4)      — Grace

Step 2: Create the First Stream Table — Recursive Hierarchy

Our first stream table flattens the department tree. For every department, it computes the full path from the root and the depth level. This uses WITH RECURSIVE — a SQL construct that can’t be differentiated with simple algebraic rules (the recursion depends on itself), but pg_trickle handles it using incremental strategies (semi-naive evaluation for inserts, Delete-and-Rederive for mixed changes) that we’ll explain later.

SELECT pgtrickle.create_stream_table(
    name         => 'department_tree',
    query        => $$
    WITH RECURSIVE tree AS (
        -- Base case: root departments (no parent)
        SELECT id, name, parent_id, name AS path, 0 AS depth
        FROM departments
        WHERE parent_id IS NULL

        UNION ALL

        -- Recursive step: children join back to the tree
        SELECT d.id, d.name, d.parent_id,
               tree.path || ' > ' || d.name AS path,
               tree.depth + 1
        FROM departments d
        JOIN tree ON d.parent_id = tree.id
    )
    SELECT id, name, parent_id, path, depth FROM tree
    $$,
    schedule     => '1m'
);

New in v0.2.0: create_stream_table also accepts diamond_consistency ('none' or 'atomic') and diamond_schedule_policy ('fastest' or 'slowest') parameters for managing diamond-shaped dependency graphs. Schedules can also be specified as cron expressions (e.g., '*/5 * * * *', '@hourly'). See SQL_REFERENCE.md for the full parameter list.

What just happened?

That single function call did a lot of work atomically (all in one transaction):

  1. Parsed the defining query into an operator tree — identifying the recursive CTE, the scan on departments, the join, the union
  2. Created a storage table called department_tree in the public schema — a real PostgreSQL heap table with columns matching the SELECT output, plus internal columns __pgt_row_id (a hash used to track individual rows)
  3. Installed CDC triggers on the departments table — lightweight AFTER INSERT OR UPDATE OR DELETE row-level triggers that will capture every future change
  4. Created a change buffer table in the pgtrickle_changes schema — this is where the triggers write captured changes
  5. Ran an initial full refresh — executed the recursive query against the current data and populated the storage table
  6. Registered the stream table in pg_trickle’s catalog with a 1-minute refresh schedule

Query it immediately — it’s already populated:

SELECT * FROM department_tree ORDER BY path;

Expected output:

 id |    name     | parent_id |            path             | depth
----+-------------+-----------+-----------------------------+-------
  1 | Company     |           | Company                     |     0
  2 | Engineering |         1 | Company > Engineering       |     1
  5 | Backend     |         2 | Company > Engineering > Backend  | 2
  6 | Frontend    |         2 | Company > Engineering > Frontend | 2
  7 | Platform    |         2 | Company > Engineering > Platform | 2
  4 | Operations  |         1 | Company > Operations        |     1
  3 | Sales       |         1 | Company > Sales             |     1

This is a real PostgreSQL table — you can create indexes on it, join it in other queries, reference it in views, or even use it as a source for other stream tables. pg_trickle keeps it in sync automatically.

Key insight: The recursive query that computes paths and depths would normally need to be re-run manually (or via REFRESH MATERIALIZED VIEW). With pg_trickle, it stays fresh — any change to the departments table is automatically reflected within the schedule bound (1 minute here).

Step 3: Chain Stream Tables — Build the Downstream Layers

Now create department_stats. The twist: instead of joining directly against departments, it joins against department_tree — the stream table we just created. This creates a chain: changes to departments update department_tree, whose changes then trigger department_stats to update.

This demonstrates how pg_trickle builds a DAG — a directed acyclic graph of stream tables — and automatically schedules refreshes in topological order.

SELECT pgtrickle.create_stream_table(
    name         => 'department_stats',
    query        => $$
    SELECT
        t.id          AS department_id,
        t.name        AS department_name,
        t.path        AS full_path,
        t.depth,
        COUNT(e.id)                    AS headcount,
        COALESCE(SUM(e.salary), 0)     AS total_salary,
        COALESCE(AVG(e.salary), 0)     AS avg_salary
    FROM department_tree t
    LEFT JOIN employees e ON e.department_id = t.id
    GROUP BY t.id, t.name, t.path, t.depth
    $$,
    schedule     => 'calculated'      -- CALCULATED: inherit schedule from downstream; see explanation below
);

What just happened — and why this one is different?

Like before, pg_trickle parsed the query, created a storage table, and set up CDC. But department_stats depends on department_tree, not a base table — so no new triggers were installed. Instead, pg_trickle registered department_tree as an upstream dependency in the DAG.

The schedule is NULL (CALCULATED mode), which means: “don’t give this table its own schedule — inherit the tightest schedule of any downstream table that queries it”. Since no other stream table has been created yet, it will be refreshed on demand or when a downstream dependent triggers it.

The query has no recursive CTE, so pg_trickle uses algebraic differentiation:

  1. Decomposed into operators: Scan(department_tree) → LEFT JOIN → Scan(employees) → Aggregate(GROUP BY + COUNT/SUM/AVG) → Project
  2. Derived a differentiation rule for each:
    • Δ(Scan) = read only change buffer rows (not the full table)
    • Δ(LEFT JOIN) = join change rows from one side against the full other side
    • Δ(Aggregate) = for COUNT/SUM/AVG, add or subtract per group — no rescan needed
  3. Composed these into a single delta query (ΔQ) that never touches unchanged rows

When one employee is inserted, the refresh reads one change buffer row, joins to find the department, and adjusts only that group’s count and sum.

Query it:

SELECT department_name, full_path, headcount, total_salary
FROM department_stats
ORDER BY full_path;

Expected output:

 department_name |                 full_path                  | headcount | total_salary
-----------------+--------------------------------------------+-----------+--------------
 Backend         | Company > Engineering > Backend            |         2 |    235000.00
 Frontend        | Company > Engineering > Frontend           |         1 |    110000.00
 Platform        | Company > Engineering > Platform           |         1 |    130000.00
 Engineering     | Company > Engineering                      |         0 |         0.00
 Operations      | Company > Operations                       |         1 |    100000.00
 Sales           | Company > Sales                            |         2 |    185000.00
 Company         | Company                                    |         0 |         0.00

Notice that the full_path column comes from department_tree — this data already went through one layer of incremental maintenance before landing here.

Add a third layer: department_report

Now add a rollup that aggregates department_stats by top-level group (depth = 1):

SELECT pgtrickle.create_stream_table(
    name         => 'department_report',
    query        => $$
    SELECT
        split_part(full_path, ' > ', 2) AS division,
        SUM(headcount)                  AS total_headcount,
        SUM(total_salary)               AS total_payroll
    FROM department_stats
    WHERE depth >= 1
    GROUP BY 1
    $$,
    schedule     => '1m'              -- this is the only explicit schedule; CALCULATED tables above inherit it
);

The DAG is now:

departments (base)  employees (base)
      │                   │
      ▼                   │
department_tree ──────────┤
   (DIFF, CALCULATED)     │
      │                   ▼
      └──────▶ department_stats
                 (DIFF, CALCULATED)
                      │
                      ▼
               department_report
                  (DIFF, 1m)   ◀── only explicit schedule

department_report drives the whole pipeline. Because it has a 1-minute schedule, pg_trickle automatically propagates that cadence upstream: department_stats and department_tree will also be refreshed within 1 minute of a base table change, in topological order, with no manual configuration.

Query the report:

SELECT * FROM department_report ORDER BY division;

  division   | total_headcount | total_payroll
-------------+-----------------+---------------
 Engineering |               4 |    475000.00
 Operations  |               1 |    100000.00
 Sales       |               2 |    185000.00

Step 4: Watch a Change Cascade Through All Three Layers

This is the heart of pg_trickle. We’ll make four changes to the base tables and watch changes propagate automatically through the three-layer DAG — each layer doing only the minimum work.

The data flow pipeline (three layers)

  Your SQL statement
       │
       ▼
  CDC trigger fires (same transaction)
  Change buffer receives one row
       │
       ▼
  Background scheduler fires (or manual refresh_stream_table)
       │
       ├──▶ [Layer 1] Refresh department_tree
       │         delta query reads change buffer
       │         MERGE touches only affected rows in department_tree
       │         department_tree's own change buffer is updated
       │
       ├──▶ [Layer 2] Refresh department_stats
       │         delta query reads department_tree's change buffer
       │         MERGE touches only affected department groups
       │
       └──▶ [Layer 3] Refresh department_report
                 delta query reads department_stats' change buffer
                 MERGE touches only affected division rows
                 All change buffers cleaned up ✓

All three layers run in a single scheduled pass, in topological order.

4a: A single INSERT ripples through all three layers

INSERT INTO employees (name, department_id, salary) VALUES
    ('Heidi', 6, 105000);  -- New Frontend engineer

What happened immediately (in your transaction): The AFTER INSERT trigger on employees fired and wrote one row to pgtrickle_changes.changes_<employees_oid>. The row contains the new values, action type I, and the LSN at the time of insert. Your transaction committed normally — no blocking.

The stream tables don’t know about Heidi yet. The change is in the buffer, waiting for the next refresh.

In production you don’t need to do anything. The background scheduler will refresh department_stats and then department_report automatically within the next minute. Manual refresh calls are only needed here to see the result immediately without waiting.

To trigger it now (optional — skip and wait ~1 minute if you prefer):

-- Refresh in source-to-sink order.
-- department_stats reads from employees (has buffered changes).
-- department_report reads from department_stats.
SELECT pgtrickle.refresh_stream_table('department_stats');
SELECT pgtrickle.refresh_stream_table('department_report');

What happened across the three layers:

Layer What ran Rows touched
department_tree No change — employees is not a source for this ST 0
department_stats Delta query: read 1 buffer row, join to Frontend, COUNT+1, SUM+105000 1 (Frontend group only)
department_report Delta query: read 1 change from dept_stats, SUM += 1 headcount, += 105000 1 (Engineering row only)

Check the result:

SELECT department_name, headcount, total_salary FROM department_stats
WHERE department_name = 'Frontend';

 department_name | headcount | total_salary
-----------------+-----------+--------------
 Frontend        |         2 |    215000.00

The 6 other groups in department_stats were not touched at all.

Contrast with a standard materialized view: REFRESH MATERIALIZED VIEW would re-scan all 8 employees, re-join with all 7 departments, re-aggregate, and update all 7 rows. With pg_trickle, the work was proportional to the 1 changed row — across all three layers.

4b: A department change cascades through the whole DAG

Now change the departments table — the root of the entire chain:

INSERT INTO departments (id, name, parent_id) VALUES
    (8, 'DevOps', 2);  -- New team under Engineering

What happened: The CDC trigger on departments fired. The change buffer for departments has one new row. None of the stream tables know about it yet.

Again, the scheduler handles this automatically. All three tables will refresh in the correct dependency order within the next minute. To see the result immediately:

-- department_tree reads from departments (has buffered changes).
-- department_stats reads from department_tree.
-- department_report reads from department_stats.
SELECT pgtrickle.refresh_stream_table('department_tree');
SELECT pgtrickle.refresh_stream_table('department_stats');
SELECT pgtrickle.refresh_stream_table('department_report');

What happened across all three layers:

Layer What ran Rows touched
department_tree Semi-naive evaluation: base case finds new dept, recursive term computes its path. Result: 1 new row 1 inserted
department_stats Delta query reads new row from dept_tree’s change buffer; DevOps has 0 employees so delta is minimal 1 inserted (headcount=0)
department_report Delta on Engineering row: headcount stays the same (DevOps has 0 employees) 0 effective changes

How the recursive CTE refresh works — unlike department_stats, recursive CTEs can’t be algebraically differentiated (the recursion references itself). pg_trickle uses incremental fixpoint strategies:

  • INSERT → semi-naive evaluation: differentiate the base case, propagate the delta through the recursive term, stopping when no new rows are produced. Only new rows inserted.
  • DELETE or UPDATE → Delete-and-Rederive (DRed): remove rows derived from deleted facts, re-derive rows that may have alternative derivation paths, handle cascades cleanly.
SELECT id, name, depth, path FROM department_tree WHERE name = 'DevOps';

 id |  name  | depth |           path                |
----+--------+-------+-------------------------------+
  8 | DevOps |         2 | Company > Engineering > DevOps |    2

The recursive CTE automatically expanded to include the new department at the correct depth and path. One inserted row in departments produced one new row in the stream table.

4c: UPDATE — A single rename that cascades everywhere

Rename “Engineering” to “R&D”:

UPDATE departments SET name = 'R&D' WHERE id = 2;

What happened in the change buffer: The CDC trigger captured the old row (name='Engineering') and the new row (name='R&D'). Both old and new values are stored so the delta can compute what to remove and what to add.

Refresh:

SELECT pgtrickle.refresh_stream_table('department_tree');
SELECT pgtrickle.refresh_stream_table('department_stats');
SELECT pgtrickle.refresh_stream_table('department_report');

What happened:

Layer Work done Result
department_tree DRed strategy: delete rows derived with old name, re-derive with new name. 5 rows updated (Engineering + 4 sub-teams) Paths now say Company > R&D > …
department_stats Delta reads 5 changed rows from dept_tree’s buffer; updates full_path column for those 5 departments 5 rows updated
department_report Division name changed: “Engineering” row replaced by “R&D” row 1 DELETE + 1 INSERT

Query to verify the cascade:

SELECT name, path FROM department_tree WHERE path LIKE '%R&D%' ORDER BY depth;

  name   |              path
---------+----------------------------------
 R&D     | Company > R&D
 Backend  | Company > R&D > Backend
 DevOps  | Company > R&D > DevOps
 Frontend | Company > R&D > Frontend
 Platform | Company > R&D > Platform

One UPDATE to a department name flowed through all three layers automatically — updating 5 + 5 + 2 rows across the chain.

4d: DELETE — Remove an employee

DELETE FROM employees WHERE name = 'Bob';

What happened: The AFTER DELETE trigger on employees fired, writing a change buffer row with action type D and Bob’s old values (department_id=5, salary=115000). The delta query will use these old values to compute the correct aggregate adjustment — it knows to subtract 115000 from Backend’s salary sum and decrement the count.

SELECT pgtrickle.refresh_stream_table('department_stats');
SELECT pgtrickle.refresh_stream_table('department_report');
SELECT * FROM department_stats WHERE department_name = 'Backend';

 department_id | department_name | headcount | total_salary | avg_salary
---------------+-----------------+-----------+--------------+------------
             5 | Backend         |         1 |    120000.00 |  120000.00

Headcount dropped from 2 → 1 and the salary aggregates updated. Again, only the Backend group was touched — the other 6 department rows were untouched.

Step 5: Automatic Scheduling — Let the DAG Drive Itself

In the examples above, we called refresh_stream_table() manually. In production you never need to do this. pg_trickle runs a background scheduler that automatically refreshes stale tables — in topological order.

How schedules propagate

We gave department_report a '1m' schedule and the two upstream tables a NULL schedule (CALCULATED mode). This is the recommended pattern:

 department_tree    (CALCULATED → inherits 1m from downstream)
       │
 department_stats   (CALCULATED → inherits 1m from downstream)
       │
 department_report  (1m — the only explicit schedule)

CALCULATED (schedule = NULL) means: compute the tightest schedule across all downstream dependents. You declare freshness requirements at the tables your application queries — the system figures out how often each upstream table needs to refresh.

What the scheduler does every second

  1. Queries the catalog for stream tables past their freshness bound
  2. Sorts them topologically (upstream first) — department_tree refreshes before department_stats, which refreshes before department_report
  3. Runs each refresh (respecting pg_trickle.max_concurrent_refreshes)
  4. Updates the last-refresh frontier

Monitoring

-- Current status of all stream tables
SELECT name, status, refresh_mode, schedule, data_timestamp, staleness
FROM pgtrickle.pgt_status();

        name                 | status |  refresh_mode | schedule |       data_timestamp        |    staleness
-----------------------------+--------+---------------+----------+-----------------------------+-----------------
 public.department_tree      | ACTIVE | DIFFERENTIAL  |          | 2026-02-26 10:30:00.123+01 | 00:00:00.877
 public.department_stats     | ACTIVE | DIFFERENTIAL  |          | 2026-02-26 10:30:00.456+01 | 00:00:00.544
 public.department_report    | ACTIVE | DIFFERENTIAL  | 1m       | 2026-02-26 10:30:00.789+01 | 00:00:00.211

-- Detailed performance stats
SELECT pgt_name, total_refreshes, avg_duration_ms, successful_refreshes
FROM pgtrickle.pg_stat_stream_tables;

-- Health check: quick triage of common issues
SELECT check_name, severity, detail FROM pgtrickle.health_check();

-- Visualize the dependency DAG
SELECT * FROM pgtrickle.dependency_tree();

-- Recent refresh timeline across all stream tables
SELECT * FROM pgtrickle.refresh_timeline(10);

-- Check CDC change buffer sizes (spotting buffer build-up)
SELECT * FROM pgtrickle.change_buffer_sizes();

See SQL_REFERENCE.md for the full list of monitoring functions including list_sources(), trigger_inventory(), and diamond_groups().

Optional: WAL-based CDC

By default pg_trickle uses triggers. If wal_level = logical is configured, set:

ALTER SYSTEM SET pg_trickle.cdc_mode = 'auto';
SELECT pg_reload_conf();

pg_trickle will automatically transition each stream table from trigger-based to WAL-based capture after the first successful refresh — reducing per-write overhead from ~2–15 μs (triggers) to near-zero (WAL-based capture adds no synchronous overhead to your DML). The transition is transparent; your queries and the refresh schedule are unaffected.

Optional: Parallel Refresh (v0.4.0+)

By default the scheduler refreshes stream tables sequentially in topological order within a single background worker. This is correct and efficient for most workloads.

For deployments with many independent stream tables, enable parallel refresh:

ALTER SYSTEM SET pg_trickle.parallel_refresh_mode = 'on';
ALTER SYSTEM SET pg_trickle.max_dynamic_refresh_workers = 4;  -- cluster-wide cap
SELECT pg_reload_conf();

Independent stream tables at the same DAG level will then refresh concurrently in separate dynamic background workers. Refresh pairs with IMMEDIATE-trigger connections and atomic consistency groups still run in a single worker for correctness.

Before enabling, ensure max_worker_processes has enough room: max_worker_processes >= 1 (launcher) + number of databases with stream tables + max_dynamic_refresh_workers (default 4) + autovacuum and other extension workers

Monitor parallel refresh:

SELECT * FROM pgtrickle.worker_pool_status();        -- live worker budget
SELECT * FROM pgtrickle.parallel_job_status(60);     -- recent jobs

See CONFIGURATION.md — Parallel Refresh for the complete tuning reference.

Step 6: Understanding the Refresh Modes and IVM Strategies

You’ve now seen the IVM strategies pg_trickle uses for incremental view maintenance. Understanding the three refresh modes and when each strategy applies helps you write efficient stream table queries.

The Three Refresh Modes

Mode When it refreshes Use case
AUTO (default) On a schedule (background) Most use cases — uses DIFFERENTIAL when possible, falls back to FULL automatically
DIFFERENTIAL On a schedule (background) Like AUTO but errors if the query can’t be differentiated
FULL On a schedule (background) Forces full recompute every cycle
IMMEDIATE Synchronously, in the same transaction as the DML Real-time dashboards, audit tables — the stream table is always up-to-date

When you omit refresh_mode, the default is 'AUTO' — it uses differential (delta-only) maintenance when the query supports it, and automatically falls back to full recomputation when it doesn’t. You only need to specify a mode explicitly for advanced cases.

IMMEDIATE mode (new in v0.2.0) maintains stream tables synchronously within the same transaction as the base table DML. It uses statement-level AFTER triggers with transition tables — no change buffers, no scheduler. The stream table is always consistent with the current transaction.

-- Create a stream table that updates in real-time
SELECT pgtrickle.create_stream_table(
    name         => 'live_headcount',
    query        => $$
    SELECT department_id, COUNT(*) AS headcount
    FROM employees
    GROUP BY department_id
    $$,
    refresh_mode => 'IMMEDIATE'
);

-- After any INSERT/UPDATE/DELETE on employees,
-- live_headcount is already up-to-date — no refresh needed!

IMMEDIATE mode supports joins, aggregates, window functions, LATERAL subqueries, and cascading IMMEDIATE stream tables. Recursive CTEs are not supported in IMMEDIATE mode (use DIFFERENTIAL instead).

You can switch between modes at any time:

-- Switch from DIFFERENTIAL to IMMEDIATE
SELECT pgtrickle.alter_stream_table('department_stats', refresh_mode => 'IMMEDIATE');

-- Switch back to DIFFERENTIAL with a schedule
SELECT pgtrickle.alter_stream_table('department_stats', refresh_mode => 'DIFFERENTIAL', schedule => '1m');

Algebraic Differentiation (used by department_stats)

For queries composed of scans, filters, joins, and algebraic aggregates (COUNT, SUM, AVG), pg_trickle can derive the IVM delta mathematically. The rules come from the theory of DBSP (Database Stream Processing):

Operator Delta Rule Cost
Scan Read only change buffer rows (not the full table) O(changes)
Filter (WHERE) Apply predicate to change rows O(changes)
Join Join change rows from one side against the full other side O(changes × lookup)
Aggregate (COUNT/SUM/AVG) Add or subtract deltas per group — no rescan O(affected groups)
Project Pass through O(changes)

The total cost is proportional to the number of changes, not the table size. For a million-row table with 10 changes, the delta query touches ~10 rows.

Incremental Strategies for Recursive CTEs (used by department_tree)

For recursive CTEs, pg_trickle can’t derive an algebraic delta because the recursion references itself. Instead it uses two complementary strategies, chosen automatically based on what changed:

Semi-naive evaluation (for INSERT-only changes): 1. Differentiate the base case — find the new seed rows 2. Propagate the delta through the recursive term, iterating until no new rows are produced 3. The result is only the new rows created by the change — not the whole tree

Delete-and-Rederive (DRed) (for DELETE or UPDATE): 1. Remove all rows derived from the old fact 2. Re-derive rows that had the old fact as one of their derivation paths (they may still be reachable via other paths) 3. Insert the newly derived rows under the new fact

Both strategies are more efficient than full recomputation — they work on the affected portion of the result set, not the entire recursive query. The MERGE only modifies rows that actually changed.

When to use which strategy?

You don’t choose — pg_trickle detects the strategy automatically based on the query structure:

Query Pattern Strategy Performance
Scan + Filter + Join + algebraic Aggregate (COUNT/SUM/AVG) Algebraic Excellent — O(changes)
Non-recursive CTEs Algebraic (inlined) CTE body is differentiated inline
MIN / MAX aggregates Semi-algebraic Uses LEAST/GREATEST merge; per-group rescan only when an extremum is deleted
STRING_AGG, ARRAY_AGG, ordered-set aggregates Group-rescan Affected groups fully re-aggregated from source
GROUPING SETS / CUBE / ROLLUP Algebraic (rewritten) Auto-expanded to UNION ALL of GROUP BY queries; CUBE capped at 64 branches
Recursive CTEs (WITH RECURSIVE) INSERT Semi-naive evaluation O(new rows derived from the change)
Recursive CTEs (WITH RECURSIVE) DELETE/UPDATE Delete-and-Rederive Re-derives rows with alternative paths; O(affected subgraph)
Window functions Partition recompute Only affected partitions recomputed
ORDER BY … LIMIT N (TopK) Scoped recomputation Re-evaluates top-N via MERGE; stores exactly N rows
IMMEDIATE mode queries In-transaction delta Same algebraic strategies, applied synchronously via transition tables

Step 7: Clean Up

When you’re done experimenting, drop the stream tables. Drop dependents before their sources:

SELECT pgtrickle.drop_stream_table('department_report');
SELECT pgtrickle.drop_stream_table('department_stats');
SELECT pgtrickle.drop_stream_table('department_tree');

DROP TABLE employees;
DROP TABLE departments;

drop_stream_table atomically removes in a single transaction: - The storage table (e.g., public.department_stats) - CDC triggers on source tables (removed only if no other stream table references the same source) - Change buffer tables in pgtrickle_changes - Catalog entries in pgtrickle.pgt_stream_tables

Summary: What You Learned

Concept What you saw
Stream tables Tables defined by a SQL query that stay automatically up to date
CDC triggers Lightweight change capture in the same transaction — no logical replication or polling required
DAG scheduling Stream tables can depend on other stream tables; refreshes run in topological order, schedules propagate upstream via CALCULATED mode
Algebraic IVM Delta queries that process only changed rows — O(changes) regardless of table size
Semi-naive / DRed Incremental strategies for WITH RECURSIVE — INSERT uses semi-naive, DELETE/UPDATE uses Delete-and-Rederive
IMMEDIATE mode Synchronous in-transaction IVM — stream tables updated within the same transaction as your DML, always consistent
TopK ORDER BY … LIMIT N queries store exactly N rows, refreshed via scoped recomputation
Diamond consistency Atomic refresh groups for diamond-shaped dependency graphs via diamond_consistency = 'atomic'
Downstream propagation A single base table write cascades through an entire chain of stream tables, automatically, in the right order
Trigger-based CDC Lightweight row-level triggers by default (no WAL configuration needed); optional transition to WAL-based capture via pg_trickle.cdc_mode = 'auto'
Parallel refresh Independent stream tables refresh concurrently in dynamic background workers via pg_trickle.parallel_refresh_mode = 'on' (v0.4.0+, default off)
Monitoring pgt_status(), health_check(), dependency_tree(), pg_stat_stream_tables, and more for freshness, timing, and error history

The key takeaway: you write to base tables — pg_trickle does the rest. Data flows downstream automatically, each layer doing the minimum work proportional to what changed, in dependency order.

Deployment Best Practices

Once you’ve built your stream tables interactively, you’ll want to deploy them reliably — via SQL migration scripts, dbt, or GitOps pipelines.

Idempotent SQL Migrations

Use create_or_replace_stream_table() in your migration scripts. It’s safe to run on every deploy:

-- migrations/V003__stream_tables.sql
-- Creates if absent, updates if definition changed, no-op if identical.

SELECT pgtrickle.create_or_replace_stream_table(
    name         => 'employee_salaries',
    query        => 'SELECT e.id, e.name, d.name AS department, e.salary
                     FROM employees e JOIN departments d ON e.department_id = d.id',
    schedule     => '2m',
    refresh_mode => 'DIFFERENTIAL'
);

SELECT pgtrickle.create_or_replace_stream_table(
    name         => 'department_stats',
    query        => 'SELECT department, COUNT(*) AS headcount, AVG(salary) AS avg_salary
                     FROM employee_salaries GROUP BY department',
    schedule     => '2m',
    refresh_mode => 'DIFFERENTIAL'
);

If someone changes the query in a later migration, create_or_replace detects the difference and migrates the storage table in place — no need to drop and recreate.

dbt Integration

With the dbt-pgtrickle package, stream tables are just dbt models with materialized='stream_table':

-- models/department_stats.sql
{{ config(
    materialized='stream_table',
    schedule='2m',
    refresh_mode='DIFFERENTIAL'
) }}

SELECT department, COUNT(*) AS headcount, AVG(salary) AS avg_salary
FROM {{ ref('employee_salaries') }}
GROUP BY department

Every dbt run calls create_or_replace_stream_table() under the hood, so deployments are always idempotent.

What’s Next?