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New in version 3.1.

The pipeline mode allows PostgreSQL client applications to send a query without having to read the result of the previously sent query. Taking advantage of the pipeline mode, a client will wait less for the server, since multiple queries/results can be sent/received in a single network roundtrip. Pipeline mode can provide a significant performance boost to the application.

Pipeline mode is most useful when the server is distant, i.e., network latency (“ping time”) is high, and also when many small operations are being performed in rapid succession. There is usually less benefit in using pipelined commands when each query takes many multiples of the client/server round-trip time to execute. A 100-statement operation run on a server 300 ms round-trip-time away would take 30 seconds in network latency alone without pipelining; with pipelining it may spend as little as 0.3 s waiting for results from the server.

The server executes statements, and returns results, in the order the client sends them. The server will begin executing the commands in the pipeline immediately, not waiting for the end of the pipeline. Note that results are buffered on the server side; the server flushes that buffer when a synchronization point is established.

The PostgreSQL documentation about:

• pipeline mode
• extended query message flow

contains many details around when it is most useful to use the pipeline mode and about errors management and interaction with transactions.

In order to understand better how the pipeline mode works, we should take a closer look at the PostgreSQL client-server message flow.

During normal querying, each statement is transmitted by the client to the server as a stream of request messages, terminating with a Sync message to tell it that it should process the messages sent so far. The server will execute the statement and describe the results back as a stream of messages, terminating with a ReadyForQuery, telling the client that it may now send a new query.

For example, the statement (returning no result):

conn.execute("INSERT INTO mytable (data) VALUES (%s)", ["hello"])

results in the following two groups of messages:

and the query:

conn.execute("SELECT data FROM mytable WHERE id = %s", [1])

results in the two groups of messages:

The two statements, sent consecutively, pay the communication overhead four times, once per leg.

The pipeline mode allows the client to combine several operations in longer streams of messages to the server, then to receive more than one response in a single batch. If we execute the two operations above in a pipeline:

with conn.pipeline():
    conn.execute("INSERT INTO mytable (data) VALUES (%s)", ["hello"])
    conn.execute("SELECT data FROM mytable WHERE id = %s", [1])

they will result in a single roundtrip between the client and the server:

Psycopg supports the pipeline mode via the Connection.pipeline() method. The method is a context manager: entering the with block yields a Pipeline object. At the end of block, the connection resumes the normal operation mode.

Within the pipeline block, you can use normally one or more cursors to execute several operations, using Connection.execute(), Cursor.execute() and executemany().

>>> with conn.pipeline():
...     conn.execute("INSERT INTO mytable VALUES (%s)", ["hello"])
...     with conn.cursor() as cur:
...         cur.execute("INSERT INTO othertable VALUES (%s)", ["world"])
...         cur.executemany(
...             "INSERT INTO elsewhere VALUES (%s)",
...             [("one",), ("two",), ("four",)])

Unlike in normal mode, Psycopg will not wait for the server to receive the result of each query; the client will receive results in batches when the server flushes it output buffer.

When a flush (or a sync) is performed, all pending results are sent back to the cursors which executed them. If a cursor had run more than one query, it will receive more than one result; results after the first will be available, in their execution order, using nextset():

>>> with conn.pipeline():
...     with conn.cursor() as cur:
...        cur.execute("INSERT INTO mytable (data) VALUES (%s) RETURNING *", ["hello"])
...        cur.execute("INSERT INTO mytable (data) VALUES (%s) RETURNING *", ["world"])
...        while True:
...            print(cur.fetchall())
...            if not cur.nextset():
...                break

[(1, 'hello')]
[(2, 'world')]

If any statement encounters an error, the server aborts the current transaction and will not execute any subsequent command in the queue until the next synchronization point; a PipelineAborted exception is raised for each such command. Query processing resumes after the synchronization point.

Certain features are not available in pipeline mode, including:

• COPY is not supported in pipeline mode by PostgreSQL.
• Cursor.stream() doesn’t make sense in pipeline mode (its job is the opposite of batching!)
• ServerCursor are currently not implemented in pipeline mode.

Starting from Psycopg 3.1, executemany() makes use internally of the pipeline mode; as a consequence there is no need to handle a pipeline block just to call executemany() once.

Flushing query results to the client can happen either when a synchronization point is established by Psycopg:

• using the Pipeline.sync() method;
• on Connection.commit() or rollback();
• at the end of a Pipeline block;
• possibly when opening a nested Pipeline block;
• using a fetch method such as Cursor.fetchone() (which only flushes the query but doesn’t issue a Sync and doesn’t reset a pipeline state error).

The server might perform a flush on its own initiative, for instance when the output buffer is full.

Note that, even in autocommit, the server wraps the statements sent in pipeline mode in an implicit transaction, which will be only committed when the Sync is received. As such, a failure in a group of statements will probably invalidate the effect of statements executed after the previous Sync, and will propagate to the following Sync.

For example, in the following block:

>>> with psycopg.connect(autocommit=True) as conn:
...     with conn.pipeline() as p, conn.cursor() as cur:
...         try:
...             cur.execute("INSERT INTO mytable (data) VALUES (%s)", ["one"])
...             cur.execute("INSERT INTO no_such_table (data) VALUES (%s)", ["two"])
...             conn.execute("INSERT INTO mytable (data) VALUES (%s)", ["three"])
...             p.sync()
...         except psycopg.errors.UndefinedTable:
...             pass
...         cur.execute("INSERT INTO mytable (data) VALUES (%s)", ["four"])

there will be an error in the block, relation "no_such_table" does not exist caused by the insert two, but probably raised by the sync() call. At at the end of the block, the table will contain:

=# SELECT * FROM mytable;
+----+------+
| id | data |
+----+------+
|  2 | four |
+----+------+
(1 row)

because:

• the value 1 of the sequence is consumed by the statement one, but the record discarded because of the error in the same implicit transaction;
• the statement three is not executed because the pipeline is aborted (so it doesn’t consume a sequence item);
• the statement four is executed with success after the Sync has terminated the failed transaction.

The exact Python statement where an exception caused by a server error is raised is somewhat arbitrary: it depends on when the server flushes its buffered result.

If you want to make sure that a group of statements is applied atomically by the server, do make use of transaction methods such as commit() or transaction(): these methods will also sync the pipeline and raise an exception if there was any error in the commands executed so far.

The Pipeline mode is an experimental feature.

Its behaviour, especially around error conditions and concurrency, hasn’t been explored as much as the normal request-response messages pattern, and its async nature makes it inherently more complex.

As we gain more experience and feedback (which is welcome), we might find bugs and shortcomings forcing us to change the current interface or behaviour.

The pipeline mode is available on any currently supported PostgreSQL version, but, in order to make use of it, the client must use a libpq from PostgreSQL 14 or higher. You can use Pipeline.is_supported() to make sure your client has the right library.