Good performance is essential for today’s database-based applications. As systems handle more users and more data, delays caused by network round trips and repeated query calls become easier to notice. Even a strong database like PostgreSQL can feel slow when an application sends many small queries one by one using the usual request-and-reply approach.
PostgreSQL Pipeline Mode was created to handle this situation. It lets an application send several SQL commands to the server in one flow, without waiting for each result before sending the next. This reduces waiting time and helps the database process work more efficiently.
What Is Pipeline Mode in PostgreSQL
In a regular PostgreSQL connection, queries are handled one at a time. The client sends a query, waits for the server to finish it, gets the result, and only then sends the next query. This approach is easy to understand and works well, but it becomes slow when an application needs to run many queries, especially when there is network delay.
Pipeline mode works differently. It lets the client send several SQL commands in a row without waiting for a response after each one. The server keeps these commands in order, runs them one by one, and then sends the results back in the same order. The client receives the results after the group of queries is processed.
By avoiding the pause between each query, pipeline mode cuts down network delays and helps queries complete faster.
Traditional Query Flow (Blocking)
Client ? Send Query ? Wait ? Get Result ? Send Next Query ? Wait ? Get Result
Pipeline Mode Flow (Non-Blocking)
Client ? Send Many Queries ? Server Executes ? Client Receives All Results Together
Why Pipeline Mode Is Important for Database Administrators
For database administrators, performance and reliability are constant concerns. Pipeline mode directly supports these goals by enabling faster batch operations with predictable execution behavior.
It is especially useful in scenarios such as bulk data loading, data migration, nightly ETL jobs, reporting systems, ERP platforms like Odoo, background processing engines, and log ingestion services. It also helps reduce idle connection time, allowing better utilization of database resources.
Test Tables Used in This Article
The following schema is used throughout the examples.
CREATE TABLE orders (
id SERIAL PRIMARY KEY,
customer VARCHAR(100),
amount NUMERIC(10,2),
status VARCHAR(20) DEFAULT 'pending',
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
CREATE TABLE customers (
id SERIAL PRIMARY KEY,
name VARCHAR(100),
email VARCHAR(100),
total_spent NUMERIC(10,2) DEFAULT 0
);
CREATE TABLE order_items (
id SERIAL PRIMARY KEY,
order_id INTEGER REFERENCES orders(id),
product VARCHAR(100),
quantity INTEGER,
price NUMERIC(10,2)
);
Starting with a Basic Pipeline Example
The most basic pipeline example shows how multiple statements can be executed together.
\startpipeline
SELECT 'Starting batch operations';
SELECT 'Processing data';
SELECT 'Operations complete';
\endpipeline
You get a result like this
postgres=*# \endpipeline
?column?
---------------------------
Starting batch operations
(1 row)
?column?
-----------------
Processing data
(1 row)
?column?
---------------------
Operations complete
(1 row)
All three queries are sent to the server together and executed in order. The results are returned as a single batch. This confirms that pipeline mode is active and functioning.
Using Pipeline Mode for Batch Inserts
Batch inserts are one of the most common and effective uses of pipeline mode.
\startpipeline
INSERT INTO customers (name, email) VALUES ('Alice Johnson', 'alice@example.com');
INSERT INTO customers (name, email) VALUES ('Bob Smith', 'bob@example.com');
INSERT INTO customers (name, email) VALUES ('Carol White', 'carol@example.com');
INSERT INTO customers (name, email) VALUES ('David Brown', 'david@example.com');
INSERT INTO customers (name, email) VALUES ('Eve Davis', 'eve@example.com');
\endpipeline
All insert statements are transmitted to the server in one network round-trip. On networks with moderate latency, this alone can produce several times faster execution compared to traditional inserts.
After the \endpipeline command , the 5 inserts are done and you get result like this
postgres=*# \endpipeline
INSERT 0 1
INSERT 0 1
INSERT 0 1
INSERT 0 1
INSERT 0 1
Combining Prepared Statements with Pipeline Mode
Prepared statements reduce query parsing and planning overhead. When combined with pipeline mode, they deliver maximum efficiency for large-scale data ingestion.
PREPARE insert_customer(VARCHAR, VARCHAR) AS
INSERT INTO customers (name, email) VALUES ($1, $2);
\startpipeline
EXECUTE insert_customer('Frank Miller', 'frank@example.com');
EXECUTE insert_customer('Grace Lee', 'grace@example.com');
EXECUTE insert_customer('Henry Wilson', 'henry@example.com');
EXECUTE insert_customer('Iris Taylor', 'iris@example.com');
EXECUTE insert_customer('Jack Anderson', 'jack@example.com');
\endpipeline
DEALLOCATE insert_customer;
The database parses and optimizes the statement only once. Each execution inside the pipeline simply binds parameters and runs the cached execution plan.
Using Sync Points for Dependent Operations
Some pipelines contain commands that depend on the results of earlier statements. In such cases, synchronization points are required.
\startpipeline
INSERT INTO orders (customer, amount) VALUES ('Alice Johnson', 1500.00);
INSERT INTO orders (customer, amount) VALUES ('Bob Smith', 2200.00);
INSERT INTO orders (customer, amount) VALUES ('Carol White', 999.99);
\syncpipeline
SELECT customer, amount, created_at
FROM orders
WHERE amount > 1000;
\startpipeline
\syncpipeline
UPDATE orders
SET status = 'approved'
WHERE amount > 1000;
\endpipeline
The first synchronization point ensures that all inserts complete before the select query runs. The second synchronization point ensures that the update executes only after the select finishes. This guarantees predictable execution order while still keeping the network efficient.
Executing Transactions Inside a Pipeline
Pipeline mode fully supports ACID-compliant transactions.
\startpipeline
BEGIN;
UPDATE customers
SET total_spent = total_spent - 100
WHERE name = 'Alice Johnson';
UPDATE customers
SET total_spent = total_spent + 100
WHERE name = 'Bob Smith';
\syncpipeline
SELECT name, total_spent
FROM customers
WHERE name IN ('Alice Johnson', 'Bob Smith');
\syncpipeline
COMMIT;
\endpipeline
All changes remain atomic. If a failure occurs before the commit, the transaction rolls back exactly as it would in traditional execution mode.
Error Handling in Pipeline Mode
Pipeline mode stops execution of the current batch when an error occurs but continues after the next synchronization point.
\startpipeline
INSERT INTO orders (customer, amount) VALUES ('Good Insert', 100);
INSERT INTO orders (id, customer, amount)
VALUES (1, 'Duplicate Key', 999);
INSERT INTO orders (customer, amount)
VALUES ('Never Inserted', 200);
\syncpipeline
SELECT COUNT(*) FROM orders;
\endpipeline
The second insert fails due to a duplicate primary key. The third insert in the same batch is skipped. After the synchronization point, the select query runs normally.
Real-World Multi-Stage Order Processing Pipeline
A production-style pipeline often contains multiple execution stages.
\startpipeline
INSERT INTO orders (customer, amount) VALUES ('Alice Johnson', 500);
INSERT INTO orders (customer, amount) VALUES ('Bob Smith', 750);
\syncpipeline
CREATE TEMP TABLE recent_orders AS
SELECT id, customer, amount
FROM orders
WHERE created_at > NOW() - INTERVAL '1 minute';
\syncpipeline
INSERT INTO order_items (order_id, product, quantity, price)
SELECT id, 'Product A', 2, 50.00
FROM recent_orders
WHERE customer = 'Alice Johnson';
INSERT INTO order_items (order_id, product, quantity, price)
SELECT id, 'Product B', 3, 75.00
FROM recent_orders
WHERE customer = 'Bob Smith';
\syncpipeline
SELECT
o.customer,
o.amount AS order_total,
COUNT(oi.id) AS item_count,
SUM(oi.quantity) AS total_items
FROM orders o
JOIN order_items oi ON o.id = oi.order_id
WHERE o.id IN (SELECT id FROM recent_orders)
GROUP BY o.id, o.customer, o.amount;
\endpipeline
This example shows how pipeline mode can be used to implement a complete business workflow with controlled execution order and high performance.
Performance Impact of Pipeline Mode
In practical testing, inserting one hundred records using traditional single-statement execution typically takes several hundred milliseconds to over a second, depending on network latency. The same operation using pipeline mode usually completes within a fraction of that time. When prepared statements are added, execution becomes even faster.
The key benefit comes from eliminating repeated network waits and minimizing server-side parsing and planning work.
When Pipeline Mode Should Be Avoided
Pipeline mode is not suitable for single interactive queries, debugging sessions, or workflows that require constant real-time decision making between statements. It should also be avoided when the client application does not properly support pipeline result handling.
PostgreSQL Pipeline Mode is a powerful execution model that dramatically improves performance for batch-oriented workloads. By eliminating unnecessary network waits and allowing large volumes of SQL commands to flow continuously to the server, it enables high-throughput data processing without sacrificing reliability or transactional safety.
For database administrators, this means faster backups, faster migrations, more efficient ETL pipelines, and better resource utilization. For developers, it means higher API performance and cleaner batch processing logic.
Pipeline mode is not merely an optimization feature. In modern high-volume systems, it is a critical tool for building scalable, production-ready PostgreSQL architectures.