问题描述
我正在使用较大的随机数作为密钥(来自另一个系统).在相当小的表(如几百万行)上进行插入和更新所花费的时间比我认为合理的长得多.
I am using large random numbers as keys (coming in from another system). Inserts and updates on fairly-small (as in a few million rows) tables are taking much longer than I think is reasonable.
我已经提炼出一个非常简单的测试来说明.在测试表中，我尝试使其尽可能简单.我的真实代码没有如此简单的布局，并具有关系和附加索引等.但是，更简单的设置可以显示相同的性能.
I have distilled a very simple test to illustrate. In the test table I've tried to make it as simple as possible; my real code does not have such a simple layout and has relations and additional indices and such. However, a simpler setup shows equivalent performance.
以下是结果:
creating the MyISAM table took 0.000 seconds
creating 1024000 rows of test data took 1.243 seconds
inserting the test data took 6.335 seconds
selecting 1023742 rows of test data took 1.435 seconds
fetching 1023742 batches of test data took 0.037 seconds
dropping the table took 0.089 seconds
creating the InnoDB table took 0.276 seconds
creating 1024000 rows of test data took 1.165 seconds
inserting the test data took 3433.268 seconds
selecting 1023748 rows of test data took 4.220 seconds
fetching 1023748 batches of test data took 0.037 seconds
dropping the table took 0.288 seconds
向MyISAM插入1M行需要6秒钟；进入InnoDB需要 3433秒！
Inserting 1M rows into MyISAM takes 6 seconds; into InnoDB takes 3433 seconds!
我做错了什么?什么是配置错误? (MySQL是具有默认设置的正常Ubuntu安装)
What am I doing wrong? What is misconfigured? (MySQL is a normal Ubuntu installation with defaults)
这是测试代码:
import sys, time, random
import MySQLdb as db
# usage: python script db_username db_password database_name
db = db.connect(host="127.0.0.1",port=3306,user=sys.argv[1],passwd=sys.argv[2],db=sys.argv[3]).cursor()
def test(engine):
start = time.time() # fine for this purpose
db.execute("""
CREATE TEMPORARY TABLE Testing123 (
k INTEGER PRIMARY KEY NOT NULL,
v VARCHAR(255) NOT NULL
) ENGINE=%s;"""%engine)
duration = time.time()-start
print "creating the %s table took %0.3f seconds"%(engine,duration)
start = time.time()
# 1 million rows in 100 chunks of 10K
data = [[(str(random.getrandbits(48)) if a&1 else int(random.getrandbits(31))) for a in xrange(10*1024*2)] for b in xrange(100)]
duration = time.time()-start
print "creating %d rows of test data took %0.3f seconds"%(sum(len(rows)/2 for rows in data),duration)
sql = "REPLACE INTO Testing123 (k,v) VALUES %s;"%("(%s,%s),"*(10*1024))[:-1]
start = time.time()
for rows in data:
db.execute(sql,rows)
duration = time.time()-start
print "inserting the test data took %0.3f seconds"%duration
# execute the query
start = time.time()
query = db.execute("SELECT k,v FROM Testing123;")
duration = time.time()-start
print "selecting %d rows of test data took %0.3f seconds"%(query,duration)
# get the rows in chunks of 10K
rows = 0
start = time.time()
while query:
batch = min(query,10*1024)
query -= batch
rows += len(db.fetchmany(batch))
duration = time.time()-start
print "fetching %d batches of test data took %0.3f seconds"%(rows,duration)
# drop the table
start = time.time()
db.execute("DROP TABLE Testing123;")
duration = time.time()-start
print "dropping the table took %0.3f seconds"%duration
test("MyISAM")
test("InnoDB")
推荐答案
InnoDB不能很好地应对随机"主键.尝试顺序键或自动递增，相信您会看到更好的性能.您的真实"键字段仍可以建立索引，但对于批量插入，最好在插入完成后一击即可删除并重新创建该索引.有兴趣查看您的基准测试！
InnoDB doesn't cope well with 'random' primary keys. Try a sequential key or auto-increment, and I believe you'll see better performance. Your 'real' key field could still be indexed, but for a bulk insert you might be better off dropping and recreating that index in one hit after the insert in complete. Would be interested to see your benchmarks for that!
一些相关问题
- Slow INSERT into InnoDB table with random PRIMARY KEY column's value
- Why do MySQL InnoDB inserts / updates on large tables get very slow when there are a few indexes?
- InnoDB inserts very slow and slowing down
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