def process(self):
Logger.init()
# 录入参数:
parsMap = self.getFormatParameter()
# 执行控制
self.dwSqlServcie = DwSql()
# 日志类型
logType = parsMap.get('logType')
if (logType == None or logType == '') :
# APP 主题 LOG
self.dwAppAccessLogStatus = self.dwAppAccessLog(parsMap)
self.dwAppActionDetailLogStatus = self.dwAppActionDetailLog(parsMap)
# WEB 主题 Log
self.dwWebVisitTrafficLogStatus = self.dwWebVisitTrafficLog(parsMap)
self.dwWebActionDetailLogStatus = self.dwWebActionDetailLog(parsMap)
elif (logType == 'dw_app_access_log' ):
self.dwAppAccessLogStatus = self.dwAppAccessLog(parsMap)
elif (logType == 'dw_app_action_detail_log' ):
self.dwAppActionDetailLogStatus = self.dwAppActionDetailLog(parsMap)
elif (logType == 'dw_web_visit_traffic_log' ):
self.dwWebVisitTrafficLogStatus = sself.dwWebVisitTrafficLog(parsMap)
elif (logType == 'dw_web_action_detail_log' ):
self.dwWebActionDetailLogStatus = self.dwWebActionDetailLog(parsMap)
def runTable(self, q, threadPoolNum):
wNum = 1
while (True):
# 队列为空的时候退出
if (q.empty() == True):
break
# 当前可消费的队列
qTableInfo = q.get()
sourceTb = str(
qTableInfo['id']
) + ': ' + qTableInfo['db_name'] + '.' + qTableInfo['tb_name']
Logger.info('线程:' + str(threadPoolNum) + ', 第: ' + str(wNum) +
' 次-> ' + str(sourceTb))
# 执行抽取任务
self.extractMysqlTable(qTableInfo)
#hiveTbName = "test." + qTableInfo['db_name'] + "__" + qTableInfo['tb_name']
#print hiveTbName
#print self.hiveModel.dropTable(hiveTbName)
q.task_done()
wNum += 1
def gatherTable(self, dbName, tbName, date):
try:
extractConf = self.getRegisterInstance(
'confModel').getExtractConf()
confSeparator = extractConf['core']['separator']
confSourceDb = extractConf['gather_table']['hive_source_db']
confTargetDb = extractConf['gather_table']['hive_target_db']
# 设置 gather 表规则
sourceDb = confSourceDb
sourceTable = dbName + confSeparator + tbName
targetDb = confTargetDb
targetTable = dbName + confSeparator + tbName
gatherTable = GatherTable()
gatherTable.setSourceDb(sourceDb)
gatherTable.setSourceTable(sourceTable)
gatherTable.setTargetDb(targetDb)
gatherTable.setTargetTable(targetTable)
gatherTable.setPartitionDate(date)
gatherTable.run()
except Exception, ex:
log = "异常-> " + str(sourceDb) + "." + str(sourceTable)
log += " -> " + str(Exception) + ":" + str(ex)
Logger.info(log)
def process(self):
Logger.init()
pars = self.getFormatParameter()
u'日期'
parsDate = pars.get('date')
if (parsDate != None):
if (parsDate == "today"):
date = self.getRegisterInstance('dateModel').getToday()
elif (parsDate == "tomorrow"):
date = self.getRegisterInstance('dateModel').getTomorrow()
elif (parsDate == "yesterday"):
date = self.getRegisterInstance('dateModel').getYesterday()
else:
date = parsDate
else:
u'默认是昨天日期 ,格式: 20151010'
date = self.getRegisterInstance('dateModel').getYesterday()
print date
self.schedulerJob(date)
self.minireportJob(date)
self.hiveMetadata(date)
self.dataWarehouse(date)
self.hdfsMonitor(date)
def extractMysqlTableThread(self):
# 等待运行数据表
sourceTableList = self.getRegisterInstance(
'biDbModel').getExtractMysqlTables(ExtractMysql.COMPLETE)
#sourceTableList = self.getRegisterInstance('biDbModel').getExtractMysqlTables(ExtractMysql.INCREMENTAL)
# 运行线程数
numThreadPool = 4
# 定义队列
q = Queue.Queue()
# 加入到队列中
for curTableInfo in sourceTableList:
q.put(curTableInfo)
# 开指定数量线程消费
for curThreadPoolNum in range(numThreadPool):
currentThread = threading.Thread(target=self.runTable,
args=(q, curThreadPoolNum))
# True：父进程不等待子进程结束, 继续执行
# False：父进程会等待所有子进程执行完毕,父进程才会退出
currentThread.setDaemon(True)
currentThread.start()
# 等到队列为空，再向下执行
q.join()
Logger.info('执行完成~')
return True
def extractMysqlTableThread(self):
# 等待运行数据表
sourceTableList = self.getRegisterInstance(
'biDbModel').getExtractMysqlTables()
# 运行线程数
numThreadPool = 2
# 定义队列
q = Queue.Queue()
# 加入到队列中
for curTableInfo in sourceTableList:
q.put(curTableInfo)
# 开指定数量线程消费
for curThreadPoolNum in range(numThreadPool):
currentThread = threading.Thread(target=self.runTable,
args=(q, curThreadPoolNum))
# 父进程不等待子进程结束,继续执行
currentThread.setDaemon(True)
currentThread.start()
sleep(5)
# 等到队列为空，再向下执行
q.join()
Logger.info('执行完成~')
def init(self):
DwServiceCore.init(self)
Logger.init()
self.ubaSqlPath = self.getDwCoreInstance().SystemPath(
'basePath') + '/uba_log/uba_sql'
def runDwSqlProcess(self, parsMap):
status = False
try :
Logger.init()
sqlFile = parsMap.get('sql')
# sql file 文件
if (sqlFile == None):
Logger.info("sql 仓库文件不存在")
exit(1)
# 日期
parsDate = parsMap.get('date')
if (parsDate != None ):
if (parsDate == "today") :
date = self.getRegisterInstance('dateModel').getToday()
elif (parsDate == "tomorrow") :
date = self.getRegisterInstance('dateModel').getTomorrow()
elif (parsDate == "yesterday") :
date = self.getRegisterInstance('dateModel').getYesterday()
else :
date = parsDate
else :
# 默认是昨天日期 ,格式: 20151010
date = self.getRegisterInstance('dateModel').getYesterday()
# 服务器类型
serverType = parsMap.get('serverType')
if (serverType == None ):
Logger.info("serverType : hive or spark")
exit(1)
# 是否是 dw 仓库的 sql 文件
isDwSql = parsMap.get("isDwSql")
# 读取 sql 文件内容，并且格式化好时间
if (isDwSql == None):
sqlContent = self.getDwSqlContent(parsMap.get('sql'), date)
elif(isDwSql == "yes"):
sqlContent = self.getDwSqlContent(parsMap.get('sql'), date)
elif(isDwSql == "no"):
sqlContent = self.getSqlContent(parsMap.get('sql'), date)
else:
Logger.info("isDwSql 参数: [yes|no]")
exit(1)
if (serverType == 'hive'):
status = self.runSqlByHive(sqlContent, parsMap.get('runEnv'))
elif (serverType == 'spark'):
status = self.runSqlBySpark(sqlContent)
except Exception,ex:
log = "异常存储过程: "
log += " -> " + str(Exception) + ":" + str(ex)
Logger.info(log)
def initGatherTable(self):
# 开始时间
startTimestamp = self.getRegisterInstance('dateModel').getTimestamp()
# 1. 根据源数据表，创建一张 gather table
# 获取源数据表字段
sourceTableFields = self.getRegisterInstance('hiveModel').getFileds(
self.getSourceDb(), self.getSourceTable())
formatTableFieldsList = []
for curField in sourceTableFields:
formatTableFieldsList.append('`' + curField + '`')
formatTableFieldsStr = ' String,'.join(
formatTableFieldsList) + " String"
createHiveTableSql = '''
CREATE TABLE IF NOT EXISTS %s.%s (
%s
) PARTITIONED BY (
`p_dt` String
)
STORED AS ORC
''' % (self.getTargetDb(), self.getTargetTable(), formatTableFieldsStr)
# 创建数据表
createHiveTableResult = self.getRegisterInstance(
'hiveModel').createTable(createHiveTableSql)
# 2. 导入数据到 gather 表
insertSql = '''
INSERT OVERWRITE TABLE `%(gatherTable)s` PARTITION (`p_dt` = '%(partitionDate)s') SELECT * FROM %(sourceTable)s;
''' % {
'gatherTable': self.getTargetDb() + '.' + self.getTargetTable(),
'partitionDate': self.getPartitionDate(),
'sourceTable': self.getSourceDb() + '.' + self.getSourceTable()
}
# 执行导入
insertResult = self.getRegisterInstance('hiveModel').batchExecuteSql(
insertSql)
# 3. 检测执行结果
if (createHiveTableResult == True and insertResult == True):
resultCode = 0
else:
resultCode = 1
# 4. 计算执行结果写日志和打印
# 计算结束日期
diffTimestamp = self.getRegisterInstance(
'dateModel').getTimestamp() - startTimestamp
# mysql 记录日志
self.extractLog(resultCode, diffTimestamp)
# 打印日志
logStr = "(初始化 : " + str(self.getSourceDb()) + "." + str(
self.getSourceTable()) + " -> " + str(
self.getTargetDb()) + "." + str(self.getTargetTable(
)) + " Time : " + str(diffTimestamp) + ")"
Logger.info(logStr)
def shutdown(self):
Logger.info("执行结果 access_log : " + str(self.accessLogStatus))
Logger.info("执行结果 dw_access_log : " + str(self.dwAccessLogStatus))
Logger.info("执行结果 uba_app_action_log : " +
str(self.ubaAppActionLogStatus))
Logger.info("执行结果 uba_web_visit_log : " +
str(self.ubaWebVisitLogStatus))
Logger.info("执行结果 uba_web_action_log : " +
str(self.ubaWebActionLogStatus))
def sourceTableToGatherTable(self):
# 开始时间
startTimestamp = self.getRegisterInstance('dateModel').getTimestamp()
# 1. 获取表结构
sourceTableFields = self.getRegisterInstance('hiveModel').getFileds(
self.getSourceDb(), self.getSourceTable())
gatherTableFields = self.getRegisterInstance('hiveModel').getFileds(
self.getTargetDb(), self.getTargetTable())
# 2. 格式化需要导入到 gather table 的字段
fieldSql = ''
for curGatherField in gatherTableFields:
if (curGatherField == 'p_dt'): continue
if (curGatherField in sourceTableFields):
fieldSql += '`' + curGatherField + '`,'
else:
fieldSql += "'' AS " + '`' + curGatherField + '`,'
# 祛除最后的逗号
formatFieldSql = fieldSql[:-1]
# 3. 拼接 SQL
gatherTableSql = '''
INSERT OVERWRITE TABLE `%(gatherTable)s` PARTITION (`p_dt` = '%(partitionDate)s') SELECT %(fieldSql)s FROM %(sourceTable)s;
''' % {
'gatherTable': self.getTargetDb() + '.' + self.getTargetTable(),
'partitionDate': self.getPartitionDate(),
'fieldSql': formatFieldSql,
'sourceTable': self.getSourceDb() + '.' + self.getSourceTable()
}
# 执行 SQL
gatherTableResult = self.getRegisterInstance(
'hiveModel').batchExecuteSql(gatherTableSql)
# 4. 检测执行结果
if (gatherTableResult == True):
resultCode = 0
else:
resultCode = 1
# 5. 计算执行结果写日志和打印
# 计算结束日期
diffTimestamp = self.getRegisterInstance(
'dateModel').getTimestamp() - startTimestamp
# mysql 记录日志
self.extractLog(resultCode, diffTimestamp)
# 打印日志
logStr = "(执行聚合 : " + str(self.getSourceDb()) + "." + str(
self.getSourceTable()) + " -> " + str(
self.getTargetDb()) + "." + str(self.getTargetTable(
)) + " Time : " + str(diffTimestamp) + ")"
Logger.info(logStr)
def run(self):
Logger.init()
# 抽取类型(全量、增量)
curExtractType = self.getExtractType()
# 全量抽取
if (curExtractType == ExtractMysql.COMPLETE):
self.extractCompleteAction()
# 增量抽取
elif (curExtractType == ExtractMysql.INCREMENTAL):
self.extractIncrementalAction()
def run(self):
Logger.init()
# 目标数据库不存在
if (self.isExistsGatherTable() == False):
# 初始化数据表
self.initGatherTable()
else:
sourceTableNewFields = self.getSourceTableNewFields()
# 如果 source table 有新增的字段
if (len(sourceTableNewFields) > 0):
# 增加新的字段
self.alterGatherTableField(sourceTableNewFields)
# 导入
self.sourceTableToGatherTable()
else:
# 导入
self.sourceTableToGatherTable()
def hdfsMonitor(self, date):
rs = self.getHdfsMB()
self.modifyIndicatorSystem(date, 'hadoop_data', rs.get('dataMb'))
self.modifyIndicatorSystem(date, 'hadoop_hdfs', rs.get('hdfsMb'))
# 输出日志
Logger.info("------------------------------")
Logger.info("hadoop_data: " + str(rs.get('dataMb')))
Logger.info("hadoop_hdfs: " + str(rs.get('hdfsMb')))
Logger.info("------------------------------")
def process(self):
Logger.init()
# 解析参数
parameter = self.getFormatParameter()
dbName = parameter.get('dbName')
tbName = parameter.get('tbName')
date = parameter.get('date')
if (date == None):
date = self.getRegisterInstance('dateModel').getYesterdayByYmd()
# snapshot 指定数据表
if (dbName != None and tbName != None):
self.snapshotTable(dbName,tbName,date)
# snapshot 所有表
else:
self.snapshotTableAll(date)
def runTable(self, q, threadPoolNum):
wNum = 1
while (True):
# 队列为空的时候退出
if (q.empty() == True):
break
# 当前可消费的队列
qTableInfo = q.get()
sourceTb = qTableInfo['db_name'] + '.' + qTableInfo[
'tb_name'] + '.' + str(qTableInfo['id'])
Logger.info('线程:' + str(threadPoolNum) + ', 第: ' + str(wNum) +
' 次. ' + str(sourceTb))
# 执行抽取任务
self.extractMysqlTable(qTableInfo)
q.task_done()
wNum += 1
def extractIncrementalAction(self):
# 检测 hive 数据表是否存在
if (self.isExistsHiveTable() == False):
Logger.info("增量抽取控制: 初始化, 全量抽取... -> " + str(self.getTbId()) +
': ' + str(self.getSourceDb()) + "." +
str(self.getSourceTable()))
# 不存在全量抽取
self.extractCompleteAction()
else:
# 检测字段变化
# 有变化时
if (self.checkStbAndTtbFields() == True):
Logger.info("增量抽取控制: 结构发生变化, 初始化, 全量抽取... -> " +
str(self.getTbId()) + ': ' +
str(self.getSourceDb()) + "." +
str(self.getSourceTable()))
# 全量抽取
self.extractCompleteAction()
# 无变化
else:
Logger.info("增量抽取控制: 增量抽取... -> " + str(self.getTbId()) +
': ' + str(self.getSourceDb()) + "." +
str(self.getSourceTable()))
# 增量抽取
self.extractIncrementalTable()
def snapshotTable(self,dbName,tbName,date):
try :
extractConf = self.getRegisterInstance('confModel').getExtractConf()
confSeparator = extractConf['core']['separator']
confSourceDb = extractConf['snapshot_table']['hive_source_db']
confTargetDb = extractConf['snapshot_table']['hive_target_db']
# 设置 snapshot表规则
sourceDb = confSourceDb
sourceTable = dbName + confSeparator + tbName
targetDb = confTargetDb
targetTable = dbName + confSeparator + tbName + '_' + date.replace('-','')
# 开始时间
startTimestamp = self.getRegisterInstance('dateModel').getTimestamp()
# 1.生成sql
createHiveTableSql = '''
DROP TABLE IF EXISTS %(snapshotTbl)s;
CREATE TABLE IF NOT EXISTS %(snapshotTbl)s AS
SELECT * FROM %(srcTbl)s;'''%{'srcTbl':sourceDb + '.' + sourceTable,
'snapshotTbl': targetDb + '.' + targetTable}
Result = self.getRegisterInstance('hiveModel').batchExecuteSql(createHiveTableSql)
# 2.计算执行结果写日志和打印
diffTimestamp = self.getRegisterInstance('dateModel').getTimestamp() - startTimestamp
logSql = "INSERT INTO dw_service.snapshot_log (`source_db`,`source_table`,`target_db`,`target_table`,`code`,`run_time`) "
logSql += "VALUES ('%s','%s','%s','%s',%d,%d)"
logSql = logSql%(sourceDb,sourceTable,targetDb,targetTable,int(Result),diffTimestamp)
self.getRegisterInstance('biDbModel').insertData(logSql)
# 打印日志
logStr = str(sourceDb) + "." + str(sourceTable) + " -> " + str(targetDb) + "." + str(targetTable) + " Time : " + str(diffTimestamp) + ")"
Logger.info(logStr)
except Exception,ex:
log = "异常-> " + str(sourceDb) + "." + str(sourceTable)
log += " -> " + str(Exception) + ":" + str(ex)
Logger.info(log)
def process(self):
Logger.init()
# 解析参数
parameter = self.getFormatParameter()
# 运行类型
runType = parameter.get('runType')
# 串行抽取
if (runType == 'liste'):
self.extractMysqlTableListe()
# 并行抽取
elif (runType == 'thread'):
self.extractMysqlTableThread()
# 指定抽取数据表抽取
elif (parameter.get('sourceDb') != None
and parameter.get('sourceTb') != None):
self.extractMysqlTableIndependent(parameter)
# 测试
else:
self.extractMysqlTableTest()
def extractMysqlSqoop(self):
Logger.info("---------- sqoop 开始 " + str(self.getTbId()) + ': ' +
str(self.getSourceDb()) + "." +
str(self.getSourceTable()) + " ----------")
# 开始时间
startTimestamp = self.getRegisterInstance('dateModel').getTimestamp()
# 1. 删除目标 Hive 对应表
Logger.info("删除目标 Hive 对应表")
self.getRegisterInstance('hiveModel').dropTable(self.getTargetDb() +
"." +
self.getTargetTable())
# 2. 执行 Sqoop Mysql 导入到 Hive
Logger.info("执行 Sqoop Mysql 导入到 Hive")
self.getRegisterInstance('sqoopModel').setDbServer(self.getExtractDb())
self.getRegisterInstance('sqoopModel').setSourceDb(self.getSourceDb())
self.getRegisterInstance('sqoopModel').setSourceTable(
self.getSourceTable())
self.getRegisterInstance('sqoopModel').setTargetDb(self.getTargetDb())
self.getRegisterInstance('sqoopModel').setTargetTable(
self.getTargetTable())
self.getRegisterInstance('sqoopModel').setMapReduceNum(
self.getMapReduceNum())
result = self.getRegisterInstance('sqoopModel').importMysqlToHive()
# 3. 检测执行结果
if (result['code'] == 0):
resultCode = 0
else:
resultCode = result['code']
# 4. 计算执行结果写日志和打印
# 计算结束日期
diffTimestamp = self.getRegisterInstance(
'dateModel').getTimestamp() - startTimestamp
#mysql 记录日志
self.extractLog(resultCode, diffTimestamp)
# 打印日志
logStr = "全量抽取 : (Sqoop : " + str(self.getTbId()) + ': ' + str(
self.getSourceDb()) + "." + str(self.getSourceTable(
)) + " -> " + str(self.getTargetDb()) + "." + str(
self.getTargetTable()) + " Time : " + str(diffTimestamp) + ")"
Logger.info(logStr)
def runSqlBySpark(self, sqlContent):
Logger.info(sqlContent)
Logger.info("执行中.....")
u'提交到 spark jdbc 执行'
status = self.getRegisterInstance('sparkModel').batchExecuteSql(sqlContent)
u'打印日志'
Logger.info(self.runLog("运行结果",status))
return status
def schedulerJob(self, date):
sc = SchedulerJob()
# 跑存储过程
sc.storedProcedure(date)
# job 总数量
jobCount = sc.getJobCount(date)
etlJobCnStatus = self.modifyIndicatorSystem(date, 'etl_job_cn',
jobCount)
# job 总平均运行时间
jobAvg = sc.getJobTotalAvgTime(date)
etlJobRunAvgStatus = self.modifyIndicatorSystem(
date, 'etl_job_run_avg', jobAvg)
# 输出日志
Logger.info("------------------------------")
Logger.info("etl_job_cn: " + str(jobCount) + " ," +
str(etlJobCnStatus))
Logger.info("etl_job_run_avg: " + str(jobAvg) + " ," +
str(etlJobRunAvgStatus))
Logger.info("------------------------------")
def hiveMetadata(self, date):
hm = HiveMetadata()
# 跑存储过程
hm.storedProcedure(date)
# hive 数据表总条数
hive_table_cn = hm.getHiveTableCount(date)
hiveTableCnStatus = self.modifyIndicatorSystem(date, 'hive_table_cn',
hive_table_cn)
# 输出日志
Logger.info("------------------------------")
Logger.info("hive_table_cn: " + str(hive_table_cn) + " ," +
str(hiveTableCnStatus))
Logger.info("------------------------------")
def minireportJob(self, date):
mr = MinireportJob()
# 跑存储过程
mr.storedProcedure(date)
# minireprot 总数量
minireportCount = mr.getMinireportCount(date)
minireportCnStatus = self.modifyIndicatorSystem(
date, 'minireport_cn', minireportCount)
# minireprot总平均运行时间
minireportAvg = mr.getMinireportTotalAvgTime(date)
minireportRunAvgStatus = self.modifyIndicatorSystem(
date, 'minireport_run_avg', minireportAvg)
# 输出日志
Logger.info("------------------------------")
Logger.info("minireport_cn: " + str(minireportCount) + " ," +
str(minireportCnStatus))
Logger.info("minireport_run_avg: " + str(minireportAvg) + " ," +
str(minireportRunAvgStatus))
Logger.info("------------------------------")
def runSqlByHive(self, sqlContent, runEnv):
Logger.info(sqlContent)
Logger.info("执行中.....")
status = False
# 运行环境
if (runEnv == "local"):
u'提交到 Hive 本地 执行'
status = self.getRegisterInstance('hiveModel').runHiveScript(sqlContent)
u'提交到 Hive jdbc 执行'
elif (runEnv == "hiveserver2"):
status = self.getRegisterInstance('hiveModel').batchExecuteSql(sqlContent)
else :
status = self.getRegisterInstance('hiveModel').batchExecuteSql(sqlContent)
u'打印日志'
Logger.info(self.runLog("运行结果",status))
return status
def extractIncrementalTable(self):
Logger.info("---------- 增量抽取开始 " + str(self.getTbId()) + ': ' +
str(self.getSourceDb()) + "." +
str(self.getSourceTable()) + " ----------")
# 开始时间
startTimestamp = self.getRegisterInstance('dateModel').getTimestamp()
# 设置增量表属性
Logger.info("设置增量表属性")
tableInfoExt = self.getRegisterInstance(
'biDbModel').getExtractMysqlTableExt(self.getTbId())
self.setIncrementalAttribute(tableInfoExt)
# 获取增量表的属性
Logger.info("获取增量表的属性")
incTbAttr = self.getIncrementalAttribute()
primaryKey = incTbAttr['primary_key']
incrementalField = incTbAttr['incremental_field']
incrementalVal = incTbAttr['incremental_val']
conditions = incTbAttr['conditions']
# hive 目标表
targetTb = self.getTargetDb() + "." + self.getTargetTable()
# hive 增量表
incTb = targetTb + "__inc"
# 1. 删除增量抽取表
Logger.info("删除增量抽取表")
self.getRegisterInstance('hiveModel').dropTable(incTb)
# 2. 创建增量抽取表
Logger.info("创建增量抽取表")
createHiveTableSql = "CREATE TABLE " + incTb + " LIKE " + targetTb
createHiveTableResult = self.getRegisterInstance(
'hiveModel').createTable(createHiveTableSql)
incDumpSql = ""
# 3. 读取最新的增量数据到本地文件中
if (incrementalVal == ""):
# 获取目标表，最大的字段数, 已这个作为基地抽取数据
Logger.info("获取目标表，最大的字段数, 已这个作为基地抽取数据")
targetTbMaxPointVal = self.getHiveTbMaxVal(targetTb,
incrementalField)
incDumpSql = "SELECT * FROM " + self.getSourceDb(
) + "." + self.getSourceTable(
) + " WHERE " + incrementalField + conditions + "'" + str(
targetTbMaxPointVal) + "'"
# 更新 point 点
Logger.info("更新 point 点")
self.updateTableExt(tableInfoExt['id'], targetTbMaxPointVal)
else:
incDumpSql = "SELECT * FROM " + self.getSourceDb(
) + "." + self.getSourceTable(
) + " WHERE " + incrementalField + conditions + "'" + incrementalVal + "'"
Logger.info("dump 更新数据到本地")
dumpIncFile = self.getDumpFileDir() + "/" + self.getDumpFileName(
) + ".inc"
dumpIncResult = self.extractDbServerModel.mysqlDumpFile(
incDumpSql, dumpIncFile)
# 4. 上传 dump 文件到 incHiveTable 中
Logger.info("上传 dump 文件到 incHiveTable 中")
hiveLoadSql = "LOAD DATA LOCAL INPATH '" + dumpIncFile + "' OVERWRITE INTO TABLE " + incTb + ";"
hiveLoadResult = self.getRegisterInstance('hiveModel').runHiveScript(
hiveLoadSql)
# 获取增量表本次最大一条增量的增量字段值
Logger.info("获取增量表本次最大一条增量的增量字段值")
incTbMaxPointVal = self.getHiveTbMaxVal(incTb, incrementalField)
# 当抽取的增量数据为空时, 不做任何处理, 直接退出
if (incTbMaxPointVal == None):
Logger.info("增量数据为空...")
return
# 5. 执行存储过程完成增量
incHiveSql = """
INSERT OVERWRITE TABLE %(targetTb)s
SELECT *
FROM (
SELECT a.*
FROM %(targetTb)s AS a
LEFT JOIN %(incTb)s AS b
ON a.%(primaryKey)s = b.%(primaryKey)s
WHERE b.%(primaryKey)s IS NULL
) AS bs
UNION ALL
SELECT * FROM %(incTb)s
;""" % {
'targetTb': targetTb,
'incTb': incTb,
'primaryKey': primaryKey
}
Logger.info(incHiveSql)
# 6. 最终逻辑运算使用 spark sql
Logger.info("最终逻辑运算使用 spark sql")
incSqlResult = self.getRegisterInstance('sparkModel').batchExecuteSql(
incHiveSql)
incSqlResult = True
# 7. 检测执行结果
if (dumpIncResult['code'] == 0 and hiveLoadResult['code'] == 0
and createHiveTableResult == True and incSqlResult == True):
# 更新节点
if (incTbMaxPointVal != None):
self.updateTableExt(tableInfoExt['id'], incTbMaxPointVal)
resultCode = 0
else:
resultCode = 1
# 计算执行结果写日志和打印
# 计算结束日期
diffTimestamp = self.getRegisterInstance(
'dateModel').getTimestamp() - startTimestamp
# mysql 记录日志
self.extractLog(resultCode, diffTimestamp)
# 打印日志
logStr = "增量抽取 : (Dump : " + str(self.getTbId()) + ': ' + str(
self.getSourceDb()) + "." + str(self.getSourceTable(
)) + " -> " + str(self.getTargetDb()) + "." + str(
self.getTargetTable()) + " Time : " + str(diffTimestamp) + ")"
Logger.info(logStr)
def extractMysqlTable(self, tableInfo):
try:
extractConf = self.getRegisterInstance(
'confModel').getExtractConf()
# 默认表分隔符
confSeparator = extractConf['core']['separator']
# 抽取到目标 hive 数据库名
confTargetDb = extractConf['extract_mysql']['hive_target_db']
# dump 本地临时目录
confDumpFileDir = extractConf['extract_mysql']['dump_file_dir']
# 数据库配置表信息
# tb id
tbId = tableInfo['id']
# mysql 数据库源信息
dbServer = tableInfo['db_server']
# 数据源: 数据库名
dbName = tableInfo['db_name']
# 数据源: 表名
tbName = tableInfo['tb_name']
# 目标 hive 数据库名
dbTargetDbName = tableInfo['target_db_name']
# 目标 hive 表名
dbTargetTbName = tableInfo['target_tb_name']
# 抽取工具
extractTool = tableInfo['extract_tool']
# 抽取类型
extractType = tableInfo['extract_type']
# 设置 hive 表名的规则
# 当指定了抽取的目标库, 使用指定的库和表名
if (dbTargetDbName != "" and dbTargetTbName != ""):
affirmTargetDb = dbTargetDbName
affirmTargetTb = dbTargetTbName
# 没有使用则用默认的规则
else:
affirmTargetDb = confTargetDb
affirmTargetTb = dbName + confSeparator + tbName
# 实例化抽取对象
extractMysql = ExtractMysql()
# Dump 方式时的保存目录
extractMysql.setDumpFileDir(confDumpFileDir)
# 设置抽取的数据库源
if (dbServer == ExtractMysql.PRODUCE_DB):
extractMysql.setExtractDb(ExtractMysql.PRODUCE_DB)
elif (dbServer == ExtractMysql.DW_DB):
extractMysql.setExtractDb(ExtractMysql.DW_DB)
else:
Logger.info("抽取的数据源不存在！" + dbServer)
# 设置抽取类型
# 全量抽取
if (extractType == ExtractMysql.COMPLETE):
extractMysql.setExtractType(ExtractMysql.COMPLETE)
# 增量抽取
elif (extractType == ExtractMysql.INCREMENTAL):
extractMysql.setExtractType(ExtractMysql.INCREMENTAL)
else:
Logger.info("抽取数据类型不存在！" + extractType)
# 配置指定抽取的工具
if (extractTool == ExtractMysql.MYSQL_DUMP):
extractMysql.setExtractTool(ExtractMysql.MYSQL_DUMP)
elif (extractTool == ExtractMysql.SQOOP):
extractMysql.setExtractTool(ExtractMysql.SQOOP)
extractMysql.setMapReduceNum(5)
# 设置抽取表的信息
sourceDb = dbName
sourceTable = tbName
targetDb = affirmTargetDb
targetTable = affirmTargetTb
extractMysql.setTbId(tbId)
extractMysql.setSourceDb(sourceDb)
extractMysql.setSourceTable(sourceTable)
extractMysql.setTargetDb(targetDb)
extractMysql.setTargetTable(targetTable)
extractMysql.run()
except Exception, ex:
log = "异常-> 数据表: " + str(dbServer) + ": " + str(
dbName) + "." + str(tbName)
log += " -> " + str(Exception) + ":" + str(ex)
Logger.info(log)
def extractMysqlDump(self):
Logger.info("---------- mysqlDump 开始 " + str(self.getTbId()) + ': ' +
str(self.getSourceDb()) + "." +
str(self.getSourceTable()) + " ---------- ")
# 开始时间
startTimestamp = self.getRegisterInstance('dateModel').getTimestamp()
# 1. 删除目标 Hive 对应表
Logger.info("删除目标 Hive 对应表")
self.getRegisterInstance('hiveModel').dropTable(self.getTargetDb() +
"." +
self.getTargetTable())
# 2. dump mysql 数据到文件中
Logger.info("dump mysql 数据到文件中")
dumpSql = "SELECT * FROM " + self.getSourceDb(
) + "." + self.getSourceTable()
dumpFile = self.getDumpFileDir() + "/" + self.getDumpFileName()
dumpResult = self.extractDbServerModel.mysqlDumpFile(dumpSql, dumpFile)
# 3. 根据 Mysql 表结构创建 Hive 表结构
Logger.info("根据 Mysql 表结构创建 Hive 表结构")
sourceTableFields = self.getSourceTableFields()
formatTableFieldsList = []
for curField in sourceTableFields:
formatTableFieldsList.append('`' + curField + '`')
formatTableFieldsStr = ' String,'.join(
formatTableFieldsList) + " String"
createHiveTableSql = '''
CREATE TABLE IF NOT EXISTS %s.%s (
%s
) ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\\001'
COLLECTION ITEMS TERMINATED BY '\\n'
STORED AS TEXTFILE
''' % (self.getTargetDb(), self.getTargetTable(), formatTableFieldsStr)
# 执行 Hive 创建表
Logger.info("执行 Hive 创建表")
createHiveTableResult = self.getRegisterInstance(
'hiveModel').createTable(createHiveTableSql)
# 4. 上传 dump 文件到 HiveTable 中
Logger.info("上传 dump 文件到 HiveTable 中")
hiveLoadSql = "LOAD DATA LOCAL INPATH '" + self.getDumpFileDir(
) + "/" + self.getDumpFileName(
) + "' OVERWRITE INTO TABLE " + self.getTargetDb(
) + "." + self.getTargetTable() + ";"
hiveLoadResult = self.getRegisterInstance('hiveModel').runHiveScript(
hiveLoadSql)
# 5. 检测执行结果
if (dumpResult['code'] == 0 and createHiveTableResult == True
and hiveLoadResult['code'] == 0):
resultCode = 0
else:
resultCode = hiveLoadResult['code']
# 6. 计算执行结果写日志和打印
# 计算结束日期
diffTimestamp = self.getRegisterInstance(
'dateModel').getTimestamp() - startTimestamp
# mysql 记录日志
self.extractLog(resultCode, diffTimestamp)
# 打印日志
logStr = "全量抽取 : (Dump : " + str(self.getTbId()) + ': ' + str(
self.getSourceDb()) + "." + str(self.getSourceTable(
)) + " -> " + str(self.getTargetDb()) + "." + str(
self.getTargetTable()) + " Time : " + str(diffTimestamp) + ")"
Logger.info(logStr)
def dataWarehouse(self, date):
# 获取昨天日期，因为每次统一的是当前天数昨天的日期
offsetDay = self.getRegisterInstance('dateModel').getOffsetDateDay(
date, -1)
# dw_web_visit_traffic_log 数据表统计
dwWebVisitTrafficLogCn = self.getTableCountForDate(
'dw_db.dw_web_visit_traffic_log', offsetDay)
dwWebVisitTrafficLogCnStatus = self.modifyIndicatorSystem(
date, 'dw_web_visit_traffic_log', dwWebVisitTrafficLogCn)
# dw_web_action_detail_log 数据表统计
dwWebActionDetailLogCn = self.getTableCountForDate(
'dw_db.dw_web_action_detail_log', offsetDay)
dwWebActionDetailLogCnStatus = self.modifyIndicatorSystem(
date, 'dw_web_action_detail_log', dwWebActionDetailLogCn)
# dw_app_access_log 数据表统计
dwAppAccessLogCn = self.getTableCountForDate('dw_db.dw_app_access_log',
offsetDay)
dwAppAccessLogCnStatus = self.modifyIndicatorSystem(
date, 'dw_app_access_log', dwAppAccessLogCn)
# dw_app_action_detail_log 数据表统计
dwAppActionDetailLogCn = self.getTableCountForDate(
'dw_db.dw_app_action_detail_log', offsetDay)
dwAppActionDetailLogCnStatus = self.modifyIndicatorSystem(
date, 'dw_app_action_detail_log', dwAppActionDetailLogCn)
# dw_property_inventory_sd 数据表统计
dwPropertyInventorySdCn = self.getTableCountForDate(
'dw_db.dw_property_inventory_sd', offsetDay)
dwPropertyInventorySdCnStatus = self.modifyIndicatorSystem(
date, 'dw_property_inventory_sd', dwPropertyInventorySdCn)
# 输出日志
Logger.info("------------------------------")
Logger.info(str(date) + " " + str(offsetDay))
Logger.info("dw_web_visit_traffic_log: " +
str(dwWebVisitTrafficLogCn) + " ," +
str(dwWebVisitTrafficLogCnStatus))
Logger.info("dw_web_action_detail_log: " +
str(dwWebActionDetailLogCn) + " ," +
str(dwWebActionDetailLogCnStatus))
Logger.info("dw_app_access_log: " + str(dwAppAccessLogCn) + " ," +
str(dwAppAccessLogCnStatus))
Logger.info("dw_app_action_detail_log: " +
str(dwAppActionDetailLogCn) + " ," +
str(dwAppActionDetailLogCnStatus))
Logger.info("dw_property_inventory_sd: " +
str(dwPropertyInventorySdCn) + " ," +
str(dwPropertyInventorySdCnStatus))
Logger.info("------------------------------")
def shutdown(self):
Logger.info("执行结果 dw_app_access_log : " + str(self.dwAppAccessLogStatus))
Logger.info("执行结果 dw_app_action_detail_log : " + str(self.dwAppActionDetailLogStatus))
Logger.info("执行结果 dw_web_visit_traffic_log : " + str(self.dwWebVisitTrafficLogStatus))
Logger.info("执行结果 dw_web_action_detail_log : " + str(self.dwWebActionDetailLogStatus))