使用 Python (PostGIS/PostgreSQL) 进行令人尴尬的并行数据库更新

Question

我需要更新空间数据库中的每条记录，其中我有一个点数据集覆盖多边形数据集。对于每个点要素，我想分配一个键以将其与它所在的多边形要素相关联。因此，如果我的点“纽约市”位于多边形 USA 内，并且对于美国多边形“GID = 1”，我将为我的点纽约市分配“gid_fkey = 1”。

为此，我创建了以下查询。

procQuery = 'UPDATE city SET gid_fkey = gid FROM country  WHERE ST_within((SELECT the_geom FROM city WHERE wp_id = %s), country.the_geom) AND city_id = %s' % (cityID, cityID)

目前，我从另一个查询中获取 cityID 信息，该查询只选择 gid_fkey 为 NULL 的所有 cityID。本质上，我只需要遍历这些并运行前面显示的查询。由于查询仅依赖于另一个表中的静态信息，理论上所有这些过程都可以一次运行。我已经实现了下面的线程过程，但我似乎无法迁移到多处理

import psycopg2, pprint, threading, time, Queue

queue = Queue.Queue()
pyConn = psycopg2.connect("dbname='geobase_1' host='localhost'")
pyConn.set_isolation_level(0)
pyCursor1 = pyConn.cursor()

getGID = 'SELECT cityID FROM city'
pyCursor1.execute(getGID)
gidList = pyCursor1.fetchall()

class threadClass(threading.Thread):

def __init__(self, queue):
        threading.Thread.__init__(self)
        self.queue = queue

def run(self):

        while True:
            gid = self.queue.get()

            procQuery = 'UPDATE city SET gid_fkey = gid FROM country  WHERE ST_within((SELECT the_geom FROM city WHERE wp_id = %s), country.the_geom) AND city_id = %s' % (cityID, cityID)

            pyCursor2 = pyConn.cursor()                         
            pyCursor2.execute(procQuery)

            print gid[0]                    
            print 'Done'

def main():

    for i in range(4):
        t = threadClass(queue)
        t.setDaemon(True)
        t.start()

        for gid in gidList:
            queue.put(gid)

    queue.join()

main()

我什至不确定多线程是否是最佳的，但它肯定比一个接一个地更快。

我将使用的机器有四个内核（四核）和一个没有 GUI、PostgreSQL、PostGIS 和 Python 的最小 Linux 操作系统，如果这会有所不同的话。

我需要进行哪些更改才能启用这个极其简单的多处理任务？

Answer 1

好的，这是对我自己帖子的回答。干得好=D

在我的系统上从单核线程到四核多处理，速度提高了大约 150%。

import multiprocessing, time, psycopg2

class Consumer(multiprocessing.Process):

def __init__(self, task_queue, result_queue):
    multiprocessing.Process.__init__(self)
    self.task_queue = task_queue
    self.result_queue = result_queue

def run(self):
    proc_name = self.name
    while True:
        next_task = self.task_queue.get()
        if next_task is None:
            print 'Tasks Complete'
            self.task_queue.task_done()
            break            
        answer = next_task()
        self.task_queue.task_done()
        self.result_queue.put(answer)
    return


class Task(object):
def __init__(self, a):
    self.a = a

def __call__(self):        
    pyConn = psycopg2.connect("dbname='geobase_1' host = 'localhost'")
    pyConn.set_isolation_level(0)
    pyCursor1 = pyConn.cursor()

        procQuery = 'UPDATE city SET gid_fkey = gid FROM country  WHERE ST_within((SELECT the_geom FROM city WHERE city_id = %s), country.the_geom) AND city_id = %s' % (self.a, self.a)

    pyCursor1.execute(procQuery)
    print 'What is self?'
    print self.a

    return self.a

def __str__(self):
    return 'ARC'
def run(self):
    print 'IN'

if __name__ == '__main__':
tasks = multiprocessing.JoinableQueue()
results = multiprocessing.Queue()

num_consumers = multiprocessing.cpu_count() * 2
consumers = [Consumer(tasks, results) for i in xrange(num_consumers)]
for w in consumers:
    w.start()

pyConnX = psycopg2.connect("dbname='geobase_1' host = 'localhost'")
pyConnX.set_isolation_level(0)
pyCursorX = pyConnX.cursor()

pyCursorX.execute('SELECT count(*) FROM cities WHERE gid_fkey IS NULL')    
temp = pyCursorX.fetchall()    
num_job = temp[0]
num_jobs = num_job[0]

pyCursorX.execute('SELECT city_id FROM city WHERE gid_fkey IS NULL')    
cityIdListTuple = pyCursorX.fetchall()    

cityIdList = []

for x in cityIdListTuple:
    cityIdList.append(x[0])


for i in xrange(num_jobs):
    tasks.put(Task(cityIdList[i - 1]))

for i in xrange(num_consumers):
    tasks.put(None)

while num_jobs:
    result = results.get()
    print result
    num_jobs -= 1

现在我在这里发布了另一个问题：

Create DB connection and maintain on multiple processes (multiprocessing)

希望我们可以摆脱一些开销，让这个婴儿更快地加速。

Answer 2

在普通 SQL 中，可以执行以下操作：

UPDATE city ci
SET gid_fkey = co.gid 
FROM country co 
WHERE ST_within(ci.the_geom , co.the_geom) 
AND ci.city_id = _some_parameter_
        ;

如果一个城市适合多个国家/地区（导致同一目标行多次更新），则可能会出现问题，但您的数据可能并非如此。