If you’d like to build PipelineDB from source, keep reading!
Building from source
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
Build the PipelineDB core (with debug symbols)
./configure CFLAGS="-g -O0" --enable-cassert --prefix=</path/to/dev/installation> make make install
Add your dev installation path to the PATH environment variable
Test PipelineDB (optional)
Run the following command:
Bootstrap the PipelineDB environment
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
Send PipelineDB some data
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
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.