关于每一列的解释联机文档上都有,这里blocks是高水位以下的数据块数,empty_blocks是高水位以上的数据块数。Dbms_stats不计算EM
我们在分析某些语句的性能时,会分析一些信息。像表、列、索引、直方图等等,本篇主要讲表与列的统计信息收集与分析。
一、表统计信息首先创建一个测试表,,更新一些数据,加入一些约束:
CREATE TABLE t
AS
SELECT rownum AS id,
round(dbms_random.normal*1000) AS val1,
100 + round(ln(rownum/3.25+2)) AS val2,
100 + round(ln(rownum/3.25+2)) AS val3,
dbms_random.string('p',250) AS pad
FROM All_Objects
WHERE ROWNUMORDER BY dbms_random.value;
UPDATE T SET VAL1 = NULL WHERE VAL1
ALTER TABLE t ADD CONSTRAINT t_pk PRIMARY KEY(ID);
CREATE INDEX t_val1_i ON t(val1);
CREATE INDEX t_val2_i ON t(val2);
BEGIN
DBMS_STATS.GATHER_TABLE_STATS(OWNNAME => USER,
TABNAME => 'T',
ESTIMATE_PERCENT => 100,
METHOD_OPT => 'for all columns size skewonly',
CASCADE => TRUE);
END;
此时表已经搜集了统计信息,查看表的统计信息用user_tab_statistics。
SELECT NUM_ROWS, BLOCKS, EMPTY_BLOCKS, AVG_SPACE, CHAIN_CNT, AVG_ROW_LEN
FROM USER_TAB_STATISTICS
WHERE TABLE_NAME = 'T';
NUM_ROWS
BLOCKS
EMPTY_BLOCKS
AVG_SPACE
CHAIN_CNT
AVG_ROW_LEN
1000
44
0
0
0
265
关于每一列的解释联机文档上都有,这里blocks是高水位以下的数据块数,empty_blocks是高水位以上的数据块数。Dbms_stats不计算EMPTY_BLOCKS、AVG_SPACE、CHAIN_CNT。

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MySQLclusteringenhancesdatabaserobustnessandscalabilitybydistributingdataacrossmultiplenodes.ItusestheNDBenginefordatareplicationandfaulttolerance,ensuringhighavailability.Setupinvolvesconfiguringmanagement,data,andSQLnodes,withcarefulmonitoringandpe

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TooptimizeMySQLperformance,followthesesteps:1)Implementproperindexingtospeedupqueries,2)UseEXPLAINtoanalyzeandoptimizequeryperformance,3)Adjustserverconfigurationsettingslikeinnodb_buffer_pool_sizeandmax_connections,4)Usepartitioningforlargetablestoi


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