Skip to main content

A thread safe queue worker that executes SQL for multi-threaded applications.

Project description

Sqlite3 Execution Queue

version license pyversions
donate powered made

A thread safe queue worker that executes SQL for multi-threaded applications.

Hierarchy

sqlqueue
'---- SqlQueue()
    |---- sql()
    |---- _sql()
    |---- commit()
    '---- stop()

Example

python

from sqlq import *

# specify the db file, relative or absolute path
# set server=True
sqlqueue = SqlQueue(server=True, db="db.db")

# SQL execution modes
# all will return the executed SQL result immediately
SqlQueue(server=True, db="default", timeout_commit=1000).sql("SELECT * FROM table;")
SqlQueue(server=True, db="commit per 1ms", timeout_commit=1).sql("INSERT INTO table VALUES (?);", (0,))
SqlQueue(server=True, db=r"C:\somewhere\db.db").sql("INSERT INTO table VALUES (?);", ((0,),(0,)))
SqlQueue(server=True, db="../../data/db.db").sql('''
CREATE TABLE "tablea" ("a" TEXT);
DELETE TABLE "table";
''')

# stop the worker
# use it at your own risk
# otherwise data will be lost
# always commit before stopping it
sqlqueue.commit()
sqlqueue.stop()

# using sqlq with encryptedsocket
# server
from encryptedsocket import SS
from easyrsa import *
sqlqueueserver = SqlQueue(server=True, db=r"db.db")
threading.Thread(target=SS(EasyRSA(bits=1024).gen_key_pair(), sqlqueueserver.functions).start).start()
# client
sqlqueue = SqlQueue()
for i in range(10):
    # SqlQueue._sql() must not be used in socket mode
    sqlqueue.sql("INSERT INTO test VALUES (?);", (str(i)))
# server
sqlqueueserver.commit()
sqlqueueserver.stop()

# SQL execution speed
# # server mode _sql() without built-in ThreadWrapper() handler
# SqlQueue(server=True, ...)._sql() <
# # server mode sql() with built-in ThreadWrapper() handler
# SqlQueue(server=True, ...).sql() <
# # client mode sql() with two nested ThreadWrapper() handlers
# SqlQueue().sql()

# this example shows how sqlq is used
# SQL should not be executed frequently
r = (1, 5, 10, 50, 100, 200)
r = (50,)
for l in r:
    tw = ThreadWrapper(threading.Semaphore(l))
    starttime = time.time()
    result = {}  # result pool
    for i in range(l):
        def job(i):
            # return SQL execution result to result pool
            return sqlqueue._sql(threading.get_ident(), "INSERT INTO test VALUES (?);", (str(i),))
        tw.add(job, args=args(i), result=result, key=i)  # pass the pool and uid in
    tw.wait()
    # p(result)
    p(l, (time.time()-starttime)/l, time.time()-starttime)
    p(sqlqueue.sql("SELECT * FROM test;"))
    tw = ThreadWrapper(threading.Semaphore(l))
    starttime = time.time()
    for i in range(l):
        def job(i):
            sqlqueue._sql(threading.get_ident(), f"DELETE FROM test WHERE a = ?;", (str(i),))
        tw.add(job, args=args(i))
    tw.wait()
    p(l, (time.time()-starttime)/l, time.time()-starttime)
    # in order to use SqlQueue()._sql(), ThreadWrapper() is
    # recommended to queue threads, check threadwrapper for more info
    # SqlQueue()._sql() will raise execution error
    # it will only report it to the result
    # you should handle the errors separately


    starttime = time.time()
    for i in range(l):
        sqlqueue.sql("INSERT INTO test VALUES (?);", (str(i),))
    p(l, (time.time()-starttime)/l, time.time()-starttime)
    p(sqlqueue.sql("SELECT * FROM test;"))
    starttime = time.time()
    for i in range(l):
        sqlqueue.sql(f"DELETE FROM test WHERE a = ?;", (str(i),))
    p(l, (time.time()-starttime)/l, time.time()-starttime)
    p()
    sqlqueue.commit()  # manual commit
    # both manual and timeout commit always wait until
    # the current SQL execution is completed. 
    # the worker will not raise any error
    # however SqlQueue().sql() will re-raise execution error

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sqlq-0.6.2.tar.gz (4.9 kB view details)

Uploaded Source

Built Distribution

sqlq-0.6.2-py3-none-any.whl (16.9 kB view details)

Uploaded Python 3

File details

Details for the file sqlq-0.6.2.tar.gz.

File metadata

  • Download URL: sqlq-0.6.2.tar.gz
  • Upload date:
  • Size: 4.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.0

File hashes

Hashes for sqlq-0.6.2.tar.gz
Algorithm Hash digest
SHA256 5422f75040eb568c05eded83049180be8c09c4cc9c97f7fa8120c548e0517a01
MD5 2f9275fae502a390f7343833c80ed744
BLAKE2b-256 2c09e7ec20881e1bd72c96610eec5113f2513019933b3eefefe92f1bc4971837

See more details on using hashes here.

File details

Details for the file sqlq-0.6.2-py3-none-any.whl.

File metadata

  • Download URL: sqlq-0.6.2-py3-none-any.whl
  • Upload date:
  • Size: 16.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.0

File hashes

Hashes for sqlq-0.6.2-py3-none-any.whl
Algorithm Hash digest
SHA256 9f2916ebc72165d58af16c8ddadbd086c0d4d2e52837b3c4833e5b81fe7868a4
MD5 be8d0bdb51940282b2d4596adde595fa
BLAKE2b-256 e82cde9716488af3726c36893224771b824e62e8533cae60ba84de37fcad124f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page