Spark将DataFrame进行一些列处理后，需要将之写入mysql，下面是实现过程
1.mysql的信息
mysql的信息我保存在了外部的配置文件，这样方便后续的配置添加。
1 //配置文件示例： 2 [[email protected] tmp_lillcol]$ cat job.properties 3 #mysql数据库配置 4 mysql.driver=com.mysql.jdbc.Driver 5 mysql.url=jdbc:mysql://127.0.0.1:3306/database1?useSSL=false&autoReconnect=true&failOverReadOnly=false&rewriteBatchedStatements=true 6 mysql.username=user 7 mysql.password=123456
2.需要的jar依赖（sbt版本，maven的对应修改即可）
1 libraryDependencies += "org.apache.spark" % "spark-core_2.10" % "1.6.0-cdh5.7.2" 2 libraryDependencies += "org.apache.spark" % "spark-sql_2.10" % "1.6.0-cdh5.7.2" 3 libraryDependencies += "org.apache.spark" % "spark-hive_2.10" % "1.6.0-cdh5.7.2" 4 libraryDependencies += "org.apache.hbase" % "hbase-client" % "1.2.0-cdh5.7.2" 5 libraryDependencies += "org.apache.hbase" % "hbase-server" % "1.2.0-cdh5.7.2" 6 libraryDependencies += "org.apache.hbase" % "hbase-common" % "1.2.0-cdh5.7.2" 7 libraryDependencies += "org.apache.hbase" % "hbase-protocol" % "1.2.0-cdh5.7.2" 8 libraryDependencies += "mysql" % "mysql-connector-java" % "5.1.38" 9 libraryDependencies += "org.apache.spark" % "spark-streaming_2.10" % "1.6.0-cdh5.7.2" 10 libraryDependencies += "com.yammer.metrics" % "metrics-core" % "2.2.0"
3.完整实现代码
1 import java.io.FileInputStream
2 import java.sql.{Connection, DriverManager}
3 import java.util.Properties
4
5 import org.apache.spark.sql.hive.HiveContext
6 import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode}
7 import org.apache.spark.{SparkConf, SparkContext}
8
9 /**
10 * @author Administrator
11 * 2018/10/16-10:15
12 *
13 */
14 object SaveDataFrameASMysql {
15 var hdfsPath: String = ""
16 var proPath: String = ""
17 var DATE: String = ""
18
19 val sparkConf: SparkConf = new SparkConf().setAppName(getClass.getSimpleName)
20 val sc: SparkContext = new SparkContext(sparkConf)
21 val sqlContext: SQLContext = new HiveContext(sc)
22
23 def main(args: Array[String]): Unit = {
24 hdfsPath = args(0)
25 proPath = args(1)
26 //不过滤读取
27 val dim_sys_city_dict: DataFrame = readMysqlTable(sqlContext, "TestMysqlTble1", proPath)
28 dim_sys_city_dict.show(10)
29
30 //保存mysql
31 saveASMysqlTable(dim_sys_city_dict, "TestMysqlTble2", SaveMode.Append, proPath)
32 }
33
34 /**
35 * 将DataFrame保存为Mysql表
36 *
37 * @param dataFrame 需要保存的dataFrame
38 * @param tableName 保存的mysql 表名
39 * @param saveMode 保存的模式 ：Append、Overwrite、ErrorIfExists、Ignore
40 * @param proPath 配置文件的路径
41 */
42 def saveASMysqlTable(dataFrame: DataFrame, tableName: String, saveMode: SaveMode, proPath: String) = {
43 var table = tableName
44 val properties: Properties = getProPerties(proPath)
45 val prop = new Properties //配置文件中的key 与 spark 中的 key 不同 所以 创建prop 按照spark 的格式 进行配置数据库
46 prop.setProperty("user", properties.getProperty("mysql.username"))
47 prop.setProperty("password", properties.getProperty("mysql.password"))
48 prop.setProperty("driver", properties.getProperty("mysql.driver"))
49 prop.setProperty("url", properties.getProperty("mysql.url"))
50 if (saveMode == SaveMode.Overwrite) {
51 var conn: Connection = null
52 try {
53 conn = DriverManager.getConnection(
54 prop.getProperty("url"),
55 prop.getProperty("user"),
56 prop.getProperty("password")
57 )
58 val stmt = conn.createStatement
59 table = table.toUpperCase
60 stmt.execute(s"truncate table $table") //为了不删除表结构，先truncate 再Append
61 conn.close()
62 }
63 catch {
64 case e: Exception =>
65 println("MySQL Error:")
66 e.printStackTrace()
67 }
68 }
69 dataFrame.write.mode(SaveMode.Append).jdbc(prop.getProperty("url"), table, prop)
70 }
71
72 /**
73 * 获取 Mysql 表的数据
74 *
75 * @param sqlContext
76 * @param tableName 读取Mysql表的名字
77 * @param proPath 配置文件的路径
78 * @return 返回 Mysql 表的 DataFrame
79 */
80 def readMysqlTable(sqlContext: SQLContext, tableName: String, proPath: String) = {
81 val properties: Properties = getProPerties(proPath)
82 sqlContext
83 .read
84 .format("jdbc")
85 .option("url", properties.getProperty("mysql.url"))
86 .option("driver", properties.getProperty("mysql.driver"))
87 .option("user", properties.getProperty("mysql.username"))
88 .option("password", properties.getProperty("mysql.password"))
89 // .option("dbtable", tableName.toUpperCase)
90 .option("dbtable", tableName)
91 .load()
92
93 }
94
95 /**
96 * 获取 Mysql 表的数据 添加过滤条件
97 *
98 * @param sqlContext
99 * @param table 读取Mysql表的名字
100 * @param filterCondition 过滤条件
101 * @param proPath 配置文件的路径
102 * @return 返回 Mysql 表的 DataFrame
103 */
104 def readMysqlTable(sqlContext: SQLContext, table: String, filterCondition: String, proPath: String) = {
105 val properties: Properties = getProPerties(proPath)
106 var tableName = ""
107 tableName = "(select * from " + table + " where " + filterCondition + " ) as t1"
108 sqlContext
109 .read
110 .format("jdbc")
111 .option("url", properties.getProperty("mysql.url"))
112 .option("driver", properties.getProperty("mysql.driver"))
113 .option("user", properties.getProperty("mysql.username"))
114 .option("password", properties.getProperty("mysql.password"))
115 .option("dbtable", tableName)
116 .load()
117 }
118
119 /**
120 * 获取配置文件
121 *
122 * @param proPath
123 * @return
124 */
125 def getProPerties(proPath: String) = {
126 val properties: Properties = new Properties()
127 properties.load(new FileInputStream(proPath))
128 properties
129 }
130 }
4.测试
1 def main(args: Array[String]): Unit = {
2 hdfsPath = args(0)
3 proPath = args(1)
4 //不过滤读取
5 val dim_sys_city_dict: DataFrame = readMysqlTable(sqlContext, "TestMysqlTble1", proPath)
6 dim_sys_city_dict.show(10)
7
8 //保存mysql
9 saveASMysqlTable(dim_sys_city_dict, "TestMysqlTble2", SaveMode.Append, proPath)
10 }
5.运行结果数据敏感进行过处理
1 +-------+-------+---------+---------+--------+----------+---------+--------------------+----+-----------+ 2 |dict_id|city_id|city_name|city_code|group_id|group_name|area_code| bureau_id|sort|bureau_name| 3 +-------+-------+---------+---------+--------+----------+---------+--------------------+----+-----------+ 4 | 1| 249| **| **_ab| 100| **按时| **-查到|xcaasd...| 21| 张三公司| 5 | 2| 240| **| **_ab| 300| **按时| **-查到|xcaasd...| 21| 张三公司| 6 | 3| 240| **| **_ab| 100| **按时| **-查到|xcaasd...| 21| 张三公司| 7 | 4| 242| **| **_ab| 300| **按时| **-查到|xcaasd...| 01| 张三公司| 8 | 5| 246| **| **_ab| 100| **按时| **-查到|xcaasd...| 01| 张三公司| 9 | 6| 246| **| **_ab| 300| **按时| **-查到|xcaasd...| 01| 张三公司| 10 | 7| 248| **| **_ab| 200| **按时| **-查到|xcaasd...| 01| 张三公司| 11 | 8| 242| **| **_ab| 400| **按时| **-查到|xcaasd...| 01| 张三公司| 12 | 9| 247| **| **_ab| 200| **按时| **-查到|xcaasd...| 01| 张三公司| 13 | 0| 243| **| **_ab| 400| **按时| **-查到|xcaasd...| 01| 张三公司| 14 +-------+-------+---------+---------+--------+----------+---------+--------------------+----+-----------+ 15 16 mysql> desc TestMysqlTble1; 17 +-------------+-------------+------+-----+---------+-------+ 18 | Field | Type | Null | Key | Default | Extra | 19 +-------------+-------------+------+-----+---------+-------+ 20 | dict_id | varchar(32) | YES | | NULL | | 21 | city_id | varchar(32) | YES | | NULL | | 22 | city_name | varchar(32) | YES | | NULL | | 23 | city_code | varchar(32) | YES | | NULL | | 24 | group_id | varchar(32) | YES | | NULL | | 25 | group_name | varchar(32) | YES | | NULL | | 26 | area_code | varchar(32) | YES | | NULL | | 27 | bureau_id | varchar(64) | YES | | NULL | | 28 | sort | varchar(32) | YES | | NULL | | 29 | bureau_name | varchar(32) | YES | | NULL | | 30 +-------------+-------------+------+-----+---------+-------+ 31 10 rows in set (0.00 sec) 32 33 mysql> desc TestMysqlTble2; 34 +-------------+------+------+-----+---------+-------+ 35 | Field | Type | Null | Key | Default | Extra | 36 +-------------+------+------+-----+---------+-------+ 37 | dict_id | text | YES | | NULL | | 38 | city_id | text | YES | | NULL | | 39 | city_name | text | YES | | NULL | | 40 | city_code | text | YES | | NULL | | 41 | group_id | text | YES | | NULL | | 42 | group_name | text | YES | | NULL | | 43 | area_code | text | YES | | NULL | | 44 | bureau_id | text | YES | | NULL | | 45 | sort | text | YES | | NULL | | 46 | bureau_name | text | YES | | NULL | | 47 +-------------+------+------+-----+---------+-------+ 48 10 rows in set (0.00 sec) 49 50 51 mysql> select count(1) from TestMysqlTble1; 52 +----------+ 53 | count(1) | 54 +----------+ 55 | 21 | 56 +----------+ 57 1 row in set (0.00 sec) 58 59 mysql> select count(1) from TestMysqlTble2; 60 +----------+ 61 | count(1) | 62 +----------+ 63 | 21 | 64 +----------+ 65 1 row in set (0.00 sec)
6.效率问题
一开始直接这么用的时候小数据还没什么，但是数据量大一点的时候速度就不行了，于是想方设法的想优化一下，用了几个手段效果不明显，然后进去看源代码，发现了两个关键的片段
1 /**
2 * Saves the content of the [[DataFrame]] to a external database table via JDBC. In the case the
3 * table already exists in the external database, behavior of this function depends on the
4 * save mode, specified by the `mode` function (default to throwing an exception).
5 *
6 * Don‘t create too many partitions in parallel on a large cluster; otherwise Spark might crash
7 * your external database systems.
8 *
9 * @param url JDBC database url of the form `jdbc:subprotocol:subname`
10 * @param table Name of the table in the external database.
11 * @param connectionProperties JDBC database connection arguments, a list of arbitrary string
12 * tag/value. Normally at least a "user" and "password" property
13 * should be included.
14 *
15 * @since 1.4.0
16 */
17 def jdbc(url: String, table: String, connectionProperties: Properties): Unit = {
18 val props = new Properties()
19 extraOptions.foreach { case (key, value) =>
20 props.put(key, value)
21 }
22 // connectionProperties should override settings in extraOptions
23 props.putAll(connectionProperties)
24 val conn = JdbcUtils.createConnectionFactory(url, props)()
25
26 try {
27 var tableExists = JdbcUtils.tableExists(conn, url, table)
28
29 if (mode == SaveMode.Ignore && tableExists) {
30 return
31 }
32
33 if (mode == SaveMode.ErrorIfExists && tableExists) {
34 sys.error(s"Table $table already exists.")
35 }
36
37 if (mode == SaveMode.Overwrite && tableExists) {
38 JdbcUtils.dropTable(conn, table)
39 tableExists = false
40 }
41
42 // Create the table if the table didn‘t exist.
43 if (!tableExists) {
44 val schema = JdbcUtils.schemaString(df, url)
45 val sql = s"CREATE TABLE $table ($schema)"
46 val statement = conn.createStatement
47 try {
48 statement.executeUpdate(sql)
49 } finally {
50 statement.close()
51 }
52 }
53 } finally {
54 conn.close()
55 }
56
57 JdbcUtils.saveTable(df, url, table, props)//-----------------------------关键点1
58 }
59
60
61 /**
62 * Saves the RDD to the database in a single transaction.
63 */
64 def saveTable(
65 df: DataFrame,
66 url: String,
67 table: String,
68 properties: Properties) {
69 val dialect = JdbcDialects.get(url)
70 val nullTypes: Array[Int] = df.schema.fields.map { field =>
71 getJdbcType(field.dataType, dialect).jdbcNullType
72 }
73
74 val rddSchema = df.schema
75 val getConnection: () => Connection = createConnectionFactory(url, properties)
76 val batchSize = properties.getProperty("batchsize", "1000").toInt
77 df.foreachPartition { iterator => //------------------------------------关键点2
78 savePartition(getConnection, table, iterator, rddSchema, nullTypes, batchSize, dialect)
79 }
80 }
也就是说，自带的方法就是按照分区来存的，每一个分区开启一个mysql连接，所以最简单的优化方式就是在保存之前对DataFrame进行重新分区，注意数据倾斜问题，不然可能效率没有提升。
当然目前测试过最快的就是文件拿下来直接通过load data的命令导入mysql，但是这个比较麻烦。
下面是分区示例
1 def main(args: Array[String]): Unit = {
2 hdfsPath = args(0)
3 proPath = args(1)
4 //不过滤读取
5 val dim_sys_city_dict: DataFrame = readMysqlTable(sqlContext, "TestMysqlTble1", proPath)
6 dim_sys_city_dict.show(10)
7
8 //保存mysql
9 saveASMysqlTable(dim_sys_city_dict.repartition(10), "TestMysqlTble2", SaveMode.Append, proPath)
10 }
7.总结
将DataFrame写入mysql有几点需要注意的地方：
- 需要保存的表最好事先建好，否则字段类型会使用默认的，Text类型实在是耗资源，对比前后两张表，下面分别为源表TestMysqlTble1和DataFrame保存的mysql表TestMysqlTble2
1 mysql> desc TestMysqlTble1; 2 +-------------+-------------+------+-----+---------+-------+ 3 | Field | Type | Null | Key | Default | Extra | 4 +-------------+-------------+------+-----+---------+-------+ 5 | dict_id | varchar(32) | YES | | NULL | | 6 | city_id | varchar(32) | YES | | NULL | | 7 | city_name | varchar(32) | YES | | NULL | | 8 | city_code | varchar(32) | YES | | NULL | | 9 | group_id | varchar(32) | YES | | NULL | | 10 | group_name | varchar(32) | YES | | NULL | | 11 | area_code | varchar(32) | YES | | NULL | | 12 | bureau_id | varchar(64) | YES | | NULL | | 13 | sort | varchar(32) | YES | | NULL | | 14 | bureau_name | varchar(32) | YES | | NULL | | 15 +-------------+-------------+------+-----+---------+-------+ 16 10 rows in set (0.00 sec) 17 18 mysql> desc TestMysqlTble2; 19 +-------------+------+------+-----+---------+-------+ 20 | Field | Type | Null | Key | Default | Extra | 21 +-------------+------+------+-----+---------+-------+ 22 | dict_id | text | YES | | NULL | | 23 | city_id | text | YES | | NULL | | 24 | city_name | text | YES | | NULL | | 25 | city_code | text | YES | | NULL | | 26 | group_id | text | YES | | NULL | | 27 | group_name | text | YES | | NULL | | 28 | area_code | text | YES | | NULL | | 29 | bureau_id | text | YES | | NULL | | 30 | sort | text | YES | | NULL | | 31 | bureau_name | text | YES | | NULL | | 32 +-------------+------+------+-----+---------+-------+ 33 10 rows in set (0.00 sec)
- 关于 SaveMode.Overwrite
1 def jdbc(url: String, table: String, connectionProperties: Properties): Unit = {
2 val props = new Properties()
3 extraOptions.foreach { case (key, value) =>
4 props.put(key, value)
5 }
6 // connectionProperties should override settings in extraOptions
7 props.putAll(connectionProperties)
8 val conn = JdbcUtils.createConnectionFactory(url, props)()
9
10 try {
11 var tableExists = JdbcUtils.tableExists(conn, url, table)
12
13 if (mode == SaveMode.Ignore && tableExists) {
14 return
15 }
16
17 if (mode == SaveMode.ErrorIfExists && tableExists) {
18 sys.error(s"Table $table already exists.")
19 }
20
21 if (mode == SaveMode.Overwrite && tableExists) {
22 JdbcUtils.dropTable(conn, table)//----------------------------------------关键点1
23 tableExists = false
24 }
25
26 // Create the table if the table didn‘t exist.
27 if (!tableExists) {
28 val schema = JdbcUtils.schemaString(df, url)
29 val sql = s"CREATE TABLE $table ($schema)"
30 val statement = conn.createStatement
31 try {
32 statement.executeUpdate(sql)
33 } finally {
34 statement.close()
35 }
36 }
37 } finally {
38 conn.close()
39 }
40
41 JdbcUtils.saveTable(df, url, table, props)
42 }
43
44 /**
45 * Drops a table from the JDBC database.
46 */
47 def dropTable(conn: Connection, table: String): Unit = {
48 val statement = conn.createStatement
49 try {
50 statement.executeUpdate(s"DROP TABLE $table")//-------------------------------------关键点2
51 } finally {
52 statement.close()
53 }
54 }
从上述两段关键代码可以看到，在写入的时候会先判断表存不存在，SaveMode.Overwrite 的时候会执行 dropTable(conn: Connection, table: String)把原来的表删除掉，这也意味着你会失去你的表结构，新建的表会出现上一个问题都用默认类型，所以在保存的方法中我添加了下面的操作
1 if (saveMode == SaveMode.Overwrite) {
2 51 var conn: Connection = null
3 52 try {
4 53 conn = DriverManager.getConnection(
5 54 prop.getProperty("url"),
6 55 prop.getProperty("user"),
7 56 prop.getProperty("password")
8 57 )
9 58 val stmt = conn.createStatement
10 59 table = table.toUpperCase
11 60 stmt.execute(s"truncate table $table") //为了不删除表结构，先truncate 再Append
12 61 conn.close()
13 62 }
14 63 catch {
15 64 case e: Exception =>
16 65 println("MySQL Error:")
17 66 e.printStackTrace()
18 67 }
truncate仅仅是删除数据，并不删除结构。
如果表一开始不存在
如果一开始不存在需要分两种情况：
1.非SaveMode.Overwrite模式
没有问题，会直接建表，用默认的数据类型
2.SaveMode.Overwrite模式
会报错，下面是在没有TestMysqlTble2的情况下使用SaveMode.Overwrite
1 com.mysql.jdbc.exceptions.jdbc4.MySQLSyntaxErrorException: Table ‘iptv.TESTMYSQLTBLE2‘ doesn‘t exist 2 at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) 3 at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) 4 at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) 5 at java.lang.reflect.Constructor.newInstance(Constructor.java:423) 6 at com.mysql.jdbc.Util.handleNewInstance(Util.java:404) 7 at com.mysql.jdbc.Util.getInstance(Util.java:387) 8 at com.mysql.jdbc.SQLError.createSQLException(SQLError.java:939) 9 at com.mysql.jdbc.MysqlIO.checkErrorPacket(MysqlIO.java:3878) 10 at com.mysql.jdbc.MysqlIO.checkErrorPacket(MysqlIO.java:3814) 11 at com.mysql.jdbc.MysqlIO.sendCommand(MysqlIO.java:2478) 12 at com.mysql.jdbc.MysqlIO.sqlQueryDirect(MysqlIO.java:2625) 13 at com.mysql.jdbc.ConnectionImpl.execSQL(ConnectionImpl.java:2547) 14 at com.mysql.jdbc.ConnectionImpl.execSQL(ConnectionImpl.java:2505) 15 at com.mysql.jdbc.StatementImpl.executeInternal(StatementImpl.java:840) 16 at com.mysql.jdbc.StatementImpl.execute(StatementImpl.java:740) 17 at com.iptv.job.basedata.SaveDataFrameASMysql$.saveASMysqlTable(SaveDataFrameASMysql.scala:62) 18 at com.iptv.job.basedata.SaveDataFrameASMysql$.main(SaveDataFrameASMysql.scala:33) 19 at com.iptv.job.basedata.SaveDataFrameASMysql.main(SaveDataFrameASMysql.scala) 20 at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) 21 at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) 22 at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) 23 at java.lang.reflect.Method.invoke(Method.java:498) 24 at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731) 25 at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181) 26 at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206) 27 at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121) 28 at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
报错详情
1 at com.iptv.job.basedata.SaveDataFrameASMysql$.saveASMysqlTable(SaveDataFrameASMysql.scala:62) 2 生面报错位置对应的代码为 3 stmt.execute(s"truncate table $table") //为了不删除表结构，先truncate 再Append 4 即truncate需要表存在
至此，DataFrame写mysql功能实现
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原文地址：https://www.cnblogs.com/lillcol/p/9796935.html
时间： 2024-08-03 05:24:55