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 check g++ flex bison python-pip pkgconf zlib1g-dev python-dev libpq-dev libncurses-dev libcurl4-openssl-dev expect
sudo pip install -r src/test/py/requirements.txt
./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 continuous view:
pipeline
=# CREATE CONTINUOUS VIEW test_view AS SELECT key::text, 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 ('key', 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.
The generate-inserts script is useful for generating and streaming larger amounts of test data. The following invocation of generate-inserts will build a SQL multi INSERT with 100,000 tuples having random strings assigned to the key field, and random ints assigned to the value field. All of these events will be emitted to test_stream, and subsequently read by the test_view continuous view. And since our script is just generating SQL, we can pipe its output directly into the pipeline client:
bin/generate-inserts --stream test_stream --key=str --value=int --batchsize=100000 --n=1 | pipeline
Try running generate-inserts without piping it into pipeline to get an idea of what's actually happening (reduce the batchsize first!).
Let's 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
-----+-------
a | 4571
e | 4502
c | 4479
f | 4473
d | 4462
b | 4451
9 | 2358
5 | 2350
4 | 2350
7 | 2327
(10 rows)
See the LICENSE file for licensing and copyright terms.