queue_splitter(1) PgQ consumer that transports events from one queue into several target queues

SYNOPSIS


queue_splitter.py [switches] config.ini

DESCRIPTION

queue_spliter is PgQ consumer that transports events from source queue into several target queues. ev_extra1 field in each event shows into which target queue it must go. (pgq.logutriga() puts there the table name.)

One use case is to move events from OLTP database to batch processing server. By using queue spliter it is possible to move all kinds of events for batch processing with one consumer thus keeping OLTP database less crowded.

QUICK-START

Basic queue_splitter setup and usage can be summarized by the following steps:

1. pgq must be installed both in source and target databases. See pgqadm man page for details. Target database must also have pgq_ext schema installed.

2. edit a queue_splitter configuration file, say queue_splitter_sourcedb_sourceq_targetdb.ini

3. create source and target queues 

$ pgqadm.py ticker.ini create <queue>

4. launch queue splitter in daemon mode 

$ queue_splitter.py queue_splitter_sourcedb_sourceq_targetdb.ini -d

5. start producing and consuming events

CONFIG

Common configuration parameters

job_name

Name for particulat job the script does. Script will log under this name to logdb/logserver. The name is also used as default for PgQ consumer name. It should be unique.

pidfile

Location for pid file. If not given, script is disallowed to daemonize.

logfile

Location for log file.

loop_delay

If continuisly running process, how long to sleep after each work loop, in seconds. Default: 1.

connection_lifetime

Close and reconnect older database connections.

log_count

Number of log files to keep. Default: 3

log_size

Max size for one log file. File is rotated if max size is reached. Default: 10485760 (10M)

use_skylog

If set, search for [./skylog.ini, ~/.skylog.ini, /etc/skylog.ini]. If found then the file is used as config file for Pythons logging module. It allows setting up fully customizable logging setup.

Common PgQ consumer parameters

pgq_queue_name

Queue name to attach to. No default.

pgq_consumer_id

Consumers ID to use when registering. Default: %(job_name)s

queue_splitter parameters

src_db

Source database.

dst_db

Target database.

Example config file

[queue_splitter]
job_name        = queue_spliter_sourcedb_sourceq_targetdb


src_db          = dbname=sourcedb
dst_db          = dbname=targetdb


pgq_queue_name  = sourceq


logfile         = ~/log/%(job_name)s.log
pidfile         = ~/pid/%(job_name)s.pid

COMMAND LINE SWITCHES

Following switches are common to all skytools.DBScript-based Python programs.

-h, --help

show help message and exit

-q, --quiet

make program silent

-v, --verbose

make program more verbose

-d, --daemon

make program go background

Following switches are used to control already running process. The pidfile is read from config then signal is sent to process id specified there.

-r, --reload

reload config (send SIGHUP)

-s, --stop

stop program safely (send SIGINT)

-k, --kill

kill program immidiately (send SIGTERM)

USECASE

How to to process events created in secondary database with several queues but have only one queue in primary database. This also shows how to insert events into queues with regular SQL easily. 

CREATE SCHEMA queue;
CREATE TABLE queue.event1 (
     -- this should correspond to event internal structure
     -- here you can put checks that correct data is put into queue
     id int4,
     name text,
     -- not needed, but good to have:
     primary key (id)
);
-- put data into queue in urlencoded format, skip actual insert
CREATE TRIGGER redirect_queue1_trg BEFORE INSERT ON queue.event1
FOR EACH ROW EXECUTE PROCEDURE pgq.logutriga('singlequeue', 'SKIP');
-- repeat the above for event2


-- now the data can be inserted:
INSERT INTO queue.event1 (id, name) VALUES (1, 'user');

If the queue_splitter is put on "singlequeue", it spreads the event on target to queues named "queue.event1", "queue.event2", etc. This keeps PgQ load on primary database minimal both CPU-wise and maintenance-wise.

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