If you just want to get started with PipelineDB right away, head over to the download page and follow the simple installation instructions.

If you'd like to build PipelineDB from source, keep reading!

Install some dependencies first:

sudo apt-get install libreadline6 libreadline6-dev g++ flex bison python-pip pkgconf zlib1g-dev python-dev libpq-dev
sudo pip install -r src/test/py/requirements.txt

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.

./configure CFLAGS="-g -O0" --enable-cassert --prefix=</path/to/dev/installation>
make
make install

export PATH=/path/to/dev/installation/bin:$PATH

Run the following command:

make check

Create PipelineDB's physical data directories, configuration files, etc:

make bootstrap

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:

make run

Enter 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:

pipeline
=# CREATE STREAM test_stream (key integer, value integer);
CREATE STREAM
=# CREATE CONTINUOUS VIEW test_view AS SELECT key, COUNT(*) FROM test_stream GROUP BY key;
CREATE CONTINUOUS VIEW

Events can be emitted to PipelineDB streams using regular SQL INSERTS. Any 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:

pipeline
=# 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?

pipeline -c "SELECT sum(count) FROM test_view"
  sum
-------
100001
(1 row)

What were the 10 most common randomly generated keys?

pipeline -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)

See the LICENSE file for licensing and copyright terms.