Question

我尝试使用Cassandra和multiprocessing根据

中的示例同时插入行（虚拟数据）

http://www.datastax.com/dev/blog/datastax-python-driver-multiprocessing-example-for-improved-bulk-data-throughput

这是我的代码

class QueryManager(object):

concurrency = 100  # chosen to match the default in execute_concurrent_with_args

def __init__(self, session, process_count=None):
    self.pool = Pool(processes=process_count, initializer=self._setup, initargs=(session,))

@classmethod
def _setup(cls, session):
    cls.session = session
    cls.prepared = cls.session.prepare("""
INSERT INTO test_table (key1, key2, key3, key4, key5) VALUES (?, ?, ?, ?, ?)
""")

def close_pool(self):
    self.pool.close()
    self.pool.join()

def get_results(self, params):
    results = self.pool.map(_multiprocess_write, (params[n:n+self.concurrency] for n in range(0, len(params), self.concurrency)))
    return list(itertools.chain(*results))

@classmethod
def _results_from_concurrent(cls, params):
    return [results[1] for results in execute_concurrent_with_args(cls.session, cls.prepared, params)]


def _multiprocess_write(params):
    return QueryManager._results_from_concurrent(params)


if __name__ == '__main__':

    processes = 2

    # connect cluster
    cluster = Cluster(contact_points=['127.0.0.1'], port=9042)
    session = cluster.connect()

    # database name is a concatenation of client_id and system_id
    keyspace_name = 'unit_test_0'

    # drop keyspace if it already exists in a cluster
    try:
        session.execute("DROP KEYSPACE IF EXISTS " + keyspace_name)
    except:
        pass

    create_keyspace_query = "CREATE KEYSPACE " + keyspace_name \
                        + " WITH replication = {'class': 'SimpleStrategy',    'replication_factor': '1'};"
    session.execute(create_keyspace_query)

    # use a session's keyspace
    session.set_keyspace(keyspace_name)

    # drop table if it already exists in the keyspace
    try:
        session.execute("DROP TABLE IF EXISTS " + "test_table")
    except:
        pass

    # create a table for invoices in the keyspace
    create_test_table = "CREATE TABLE test_table("

    keys = "key1 text,\n" \
           "key2 text,\n" \
           "key3 text,\n" \
           "key4 text,\n" \
           "key5 text,\n"

    create_invoice_table_query += keys
    create_invoice_table_query += "PRIMARY KEY (key1))"
    session.execute(create_test_table)

    qm = QueryManager(session, processes)

    params = list()
    for row in range(100000):
        key = 'test' + str(row)
        params.append([key, 'test', 'test', 'test', 'test'])

    start = time.time()
    rows = qm.get_results(params)
    delta = time.time() - start
    log.info(fm('Cassandra inserts 100k dummy rows for ', delta, ' secs'))

当我执行代码时，我收到以下错误

TypeError: can't pickle _thread.lock objects

指向

self.pool = Pool(processes=process_count, initializer=self._setup, initargs=(session,))

Answer 1

这表明你试图在IPC边界上序列化锁定。我想这可能是因为您提供了一个Session对象作为工作者初始化函数的参数。使init函数在每个工作进程中创建一个新会话（请参阅＆＃34;每个进程的会话＆＃34;您引用的blog post部分。）

Answer 2

我知道这已经有了答案，但是我想强调一下cassandra-driver程序包中的一些更改，这些更改使此代码仍无法在python 3.7和3.18.0 cassandra-driver程序包中正常工作。

如果您查看链接的博客文章。 __init__函数不会传递session，但会传递cluster对象。即使cluster也不能作为initarg发送，因为它包含一个锁。您需要在def _setup(cls):类方法中创建它。

第二，execute_concurrent_with_args现在返回一个ResultSet，并且也无法序列化。较早版本的cassandra驱动程序包仅返回对象列表。

要修复上述代码，请更改以下两个部分：

首先，__init__和_setup方法

def __init__(self, process_count=None):
    self.pool = Pool(processes=process_count, initializer=self._setup)

@classmethod
def _setup(cls):
    cluster = Cluster()
    cls.session = cluster.connect()
    cls.prepared = cls.session.prepare("""
        INSERT INTO test_table (key1, key2, key3, key4, key5) VALUES (?, ?, ?, ?, ?)
        """)

第二，_results_from_concurrent方法

@classmethod
def _results_from_concurrent(cls, params):
    return [list(results[1]) for results in execute_concurrent_with_args(cls.session, cls.prepared, params)]

最后，如果您对与python3和cassandra-driver 3.18.0一起使用的原始DataStax博客文章中的multiprocess_execute.py的要点感兴趣，可以在这里找到：https://gist.github.com/jWolo/6127b2e57c7e24740afd7a4254cc00a3

Cassandra多处理无法腌制_thread.lock对象

2 个答案: