PipelineDB will not have new releases beyond
1.0.0, although critical bugs will still be fixed.
PipelineDB is a PostgreSQL extension for high-performance time-series aggregation, designed to power realtime reporting and analytics applications.
PipelineDB allows you to define continuous SQL queries that perpetually aggregate time-series data and store only the aggregate output in regular, queryable tables. You can think of this concept as extremely high-throughput, incrementally updated materialized views that never need to be manually refreshed.
Raw time-series data is never written to disk, making PipelineDB extremely efficient for aggregation workloads.
PipelineDB runs on 64-bit architectures and currently supports the following PostgreSQL versions:
If you just want to start using PipelineDB right away, head over to the installation docs to get going.
If you'd like to build PipelineDB from source, keep reading!
Since PipelineDB is a PostgreSQL extension, you'll need to have the PostgreSQL development packages installed to build PipelineDB.
Next you'll have to install ZeroMQ which PipelineDB uses for inter-process communication. Here's a gist with instructions to build and install ZeroMQ from source. You'll also need to install some Python dependencies if you'd like to run PipelineDB's Python test suite:
pip install -r src/test/py/requirements.txt
Once PostgreSQL is installed, you can build PipelineDB against it:
make USE_PGXS=1 make install
Run the following command:
Create PipelineDB's physical data directories, configuration files, etc:
make bootstrap only needs to be run the first time you install PipelineDB. The resources that
make bootstrap creates may continue to be used as you change and rebuild PipeineDB.
Run all of the daemons necessary for PipelineDB to operate:
Ctrl+C to shut down PipelineDB.
make run uses the binaries in the PipelineDB source root compiled by
make, so you don't need to
make install before running
make run after code changes--only
make needs to be run.
The basic development flow is:
make make run ^C # Make some code changes... make make run
Now let's generate some test data and stream it into a simple continuous view. First, create the stream and the continuous view that reads from it:
$ psql =# CREATE FOREIGN TABLE test_stream (key integer, value integer) SERVER pipelinedb; CREATE FOREIGN TABLE =# CREATE VIEW test_view WITH (action=materialize) AS SELECT key, COUNT(*) FROM test_stream GROUP BY key; CREATE VIEW
Events can be emitted to PipelineDB streams using regular SQL
INSERT target that isn't a table is considered a stream by PipelineDB, meaning streams don't need to have a schema created in advance. Let's emit a single event into the
test_stream stream since our continuous view is reading from it:
$ psql =# INSERT INTO test_stream (key, value) VALUES (0, 42); INSERT 0 1
The 1 in the
INSERT 0 1 response means that 1 event was emitted into a stream that is actually being read by a continuous query. Now let's insert some random data:
=# INSERT INTO test_stream (key, value) SELECT random() * 10, random() * 10 FROM generate_series(1, 100000); INSERT 0 100000
Query the continuous view to verify that the continuous view was properly updated. Were there actually 100,001 events counted?
$ psql -c "SELECT sum(count) FROM test_view" sum ------- 100001 (1 row)
What were the 10 most common randomly generated keys?
$ psql -c "SELECT * FROM test_view ORDER BY count DESC limit 10" key | count -----+------- 2 | 10124 8 | 10100 1 | 10042 7 | 9996 4 | 9991 5 | 9977 3 | 9963 6 | 9927 9 | 9915 10 | 4997 0 | 4969 (11 rows)