From 90ed3057988c51cc675829f72feba630c3a98f7a Mon Sep 17 00:00:00 2001 From: Dhruve Ashar Date: Fri, 29 Jun 2018 11:11:00 -0500 Subject: [PATCH 1/3] Add spark hbase connector example --- pom.xml | 28 +++++- .../scala/com/yahoo/spark/starter/README.md | 99 +++++++++++++++++++ .../starter/SparkClusterHBaseConnector.scala | 73 ++++++++++++++ 3 files changed, 197 insertions(+), 3 deletions(-) create mode 100644 src/main/scala/com/yahoo/spark/starter/README.md create mode 100644 src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala diff --git a/pom.xml b/pom.xml index df9ccf5a88f30..5c159fb985e50 100644 --- a/pom.xml +++ b/pom.xml @@ -78,6 +78,16 @@ ${hbase.version} provided + + com.databricks + spark-avro_2.11 + 3.2.0 + + + com.hortonworks + shc-core + 1.0 + @@ -116,9 +126,21 @@ org.apache.maven.plugins maven-install-plugin - - true - + + + initialize + + install-file + + + com.hortonworks + shc-core + 1.0 + jar + ${basedir}/lib/shc-core-1.1.2-2.2-s_2.11-SNAPSHOT.jar + + + net.alchim31.maven diff --git a/src/main/scala/com/yahoo/spark/starter/README.md b/src/main/scala/com/yahoo/spark/starter/README.md new file mode 100644 index 0000000000000..054293f8c5e7a --- /dev/null +++ b/src/main/scala/com/yahoo/spark/starter/README.md @@ -0,0 +1,99 @@ +#### Connecting to Apache HBase from Apache Spark using the SHC(Apache Spark - Apache HBase) Connector. +For the original project you can look up the documentation at: +* https://github.com/hortonworks-spark/shc + +For using the connector you need to use HBase Version 1.3 and above. Most of our grids (verify the status of HBase deployment) have the required version of HBase although we have 0.98 clients running on most of the gateways for backward compatibility. + + +*** + +#### Setup + +* You would need hbase 1.3 jars to be supplied with your spark job. +```bash +# Since hbase-1.3 is not available on the gateways, you need to install hbase-1.3 locally to access the jars. +yinst i hbase-1.3.2.9.1804250211_h2 -br quarantine -r ~/hbase -nosudo + +# Define an ENV variable - HBASE_JARS to point to the directory you installed hbase locally. +export HBASE_JARS=~/hbase/libexec/hbase/lib +``` + +#### Getting the connector +You can add the spark hbase connector jar in your project directory and update your pom file to package the dependencies with your application jar. Refer to the pom file in the spark-starter project for details. + +1. Copy the spark-hbase-connector jar from the spark-starter project to your project under a directory - `lib`. It can be any appropriate location. +2. Update your pom file to ensure the dependency is installed when you build your project. +```xml + + + org.apache.maven.plugins + maven-install-plugin + + + initialize + + install-file + + + com.hortonworks + shc-core + 1.0 + jar + ${basedir}/lib/shc-core-1.1.2-2.2-s_2.11-SNAPSHOT.jar + + + + + + + + com.hortonworks + shc-core + 1.0 + +``` + +* Launching in cluster mode +```bash +# launch the shell +$SPARK_HOME/bin/spark-submit --master yarn --deploy-mode cluster --conf "spark.hadoop.validateOutputSpecs=false" --jars $HBASE_JARS/hbase-protocol.jar,$HBASE_JARS/hbase-common.jar,$HBASE_JARS/hbase-client.jar,$HBASE_JARS/htrace-core-3.1.0-incubating.jar,$HBASE_JARS/hbase-server.jar,$HBASE_JARS/guava-12.0.1.jar,$HBASE_JARS/metrics-core-2.2.0.jar --files $SPARK_CONF_DIR/hbase-site.xml ... +``` + +* Launching in client mode +```bash +# launch the shell +$SPARK_HOME/bin/spark-submit --master yarn --deploy-mode client --conf "spark.hadoop.validateOutputSpecs=false" --jars $HBASE_JARS/hbase-protocol.jar,$HBASE_JARS/hbase-common.jar,$HBASE_JARS/hbase-client.jar,$HBASE_JARS/htrace-core-3.1.0-incubating.jar,$HBASE_JARS/hbase-server.jar,$HBASE_JARS/guava-12.0.1.jar,$HBASE_JARS/metrics-core-2.2.0.jar .... +``` + +* If you plan to write to HBase using the connector, set the required conf `"spark.hadoop.validateOutputSpecs=false"`. This is to workaround a bug in HBase. See YHBASE-2131 for more details. + +### Using the connector +You need to define a catalog so that a DataFrame can be mapped to an HBase table or vice-versa. This is defined in a json format. Below is an example for your reference explaining how to define your own catalog. +``` +def catalog = s"""{ + |"table":{"namespace":"default", "name":"table1"}, + |"rowkey":"key", + |"columns":{ + |"df_col0":{"cf":"rowkey", "col":"key", "type":"string"}, + |"df_col1":{"cf":"cf1", "col":"col1", "type":"boolean"}, + |"df_col2":{"cf":"cf2", "col":"col2", "type":"double"}, + |"df_col3":{"cf":"cf3", "col":"col3", "type":"float"}, + |"df_col4":{"cf":"cf4", "col":"col4", "type":"int"}, + |"df_col5":{"cf":"cf5", "col":"col5", "type":"bigint"}, + |"df_col6":{"cf":"cf6", "col":"col6", "type":"smallint"}, + |"df_col7":{"cf":"cf7", "col":"col7", "type":"string"}, + |"df_col8":{"cf":"cf8", "col":"col8", "type":"tinyint"} + |} + |}""".stripMargin +``` +* `table` - you specify the namespace and the name of the table +* `rowkey` - you specify the column/s in the hbase table that form the rowkey. Composite keys are not currently supported. +* `columns` field, you specify the name of the column in the DataFrame and its corresponding column_family, column_name and column_type in hbase. You identify the column/s which are used to form the rowkey using the column_family as rowkey. + +Note: Since a user can add any arbitrary columns to a column family, individual columns are not specified under the table schema in HBase table description. Therefore it is helpful to know the columns of interest available in hbase table while defining the catalog. + +For more examples, you can check the examples in the original repo [here](https://github.com/hortonworks-spark/shc). + + + + diff --git a/src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala b/src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala new file mode 100644 index 0000000000000..506d51a359453 --- /dev/null +++ b/src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala @@ -0,0 +1,73 @@ +package com.yahoo.spark.starter + +import org.apache.spark._ +import org.apache.spark.sql._ +import org.apache.spark.sql.execution.datasources.hbase._ +import org.apache.spark.sql.SparkSession + +case class DataRow(name: String, pet: String, horcruxes: Int, original_name: String) + +// Simple example of accessing HBase from Spark using the Spark HBase connector +object SparkClusterHBaseConnector { + + def main(args: Array[String]) { + + if (args == null || args.length < 1) { + System.err.println("Usage: SparkClusterHBaseConnector ") + System.exit(1) + } + + // Use the new 2.0 API. + val spark = SparkSession. + builder. + appName("Spark HBase Connector Example"). + getOrCreate() + + import spark.implicits._ + + val tableName = s"spark_test:${args(0)}" + + val df = Seq( DataRow("harry", "hedwig", 0, null), DataRow("voldy", "nagini", 7, "Tom")).toDF("name", "pet", "horcruxes", "original_name") + // print the original dataframe + df.show + + // define the catalog + def catalog = s"""{ + |"table":{"namespace":"spark_test", "name":"harrypotter"}, + |"rowkey":"name", + |"columns":{ + |"name":{"cf":"rowkey", "col":"name", "type":"string"}, + |"pet":{"cf":"cf1", "col":"pet", "type":"string"}, + |"horcruxes":{"cf":"cf2", "col":"horcruxes", "type":"int"}, + |"original_name":{"cf":"cf3", "col":"original_name", "type":"string"} + |} + |}""".stripMargin + + // write data to hbase + hbaseWrite(df, catalog) + + // read the data from hbase + val df2 = hbaseRead(spark, catalog) + df2.show + + spark.stop + } + + // Read data from an existing hbase table + def hbaseRead(spark: SparkSession, catalog: String): DataFrame = { + spark + .read + .options(Map(HBaseTableCatalog.tableCatalog->catalog)) + .format("org.apache.spark.sql.execution.datasources.hbase") + .load() + } + + // Write data to an hbase table + def hbaseWrite(df: DataFrame, catalog: String): Unit = { + df + .write + .options(Map(HBaseTableCatalog.tableCatalog -> catalog, HBaseTableCatalog.newTable -> "5")) + .format("org.apache.spark.sql.execution.datasources.hbase") + .save + } +} From eae30c492885dc6f183cd74b347a1c641ae28a75 Mon Sep 17 00:00:00 2001 From: Dhruve Ashar Date: Mon, 2 Jul 2018 09:56:47 -0500 Subject: [PATCH 2/3] Address review comments --- lib/shc-core-1.1.2-2.2-s_2.11-SNAPSHOT.jar | Bin 0 -> 551434 bytes pom.xml | 5 ----- .../scala/com/yahoo/spark/starter/README.md | 8 +++----- .../starter/SparkClusterHBaseConnector.scala | 11 +++++++---- 4 files changed, 10 insertions(+), 14 deletions(-) create mode 100644 lib/shc-core-1.1.2-2.2-s_2.11-SNAPSHOT.jar diff --git a/lib/shc-core-1.1.2-2.2-s_2.11-SNAPSHOT.jar b/lib/shc-core-1.1.2-2.2-s_2.11-SNAPSHOT.jar new file mode 100644 index 0000000000000000000000000000000000000000..0b8af30f778fd6633f9774f19e5138357755de63 GIT binary patch literal 551434 zcmbT7WpEr_mZoJ}tP(R@%*@Qp%ur%xmL*xt%*>Jn7Be$5Gcz;8x~F@lr+0UHW54>5 zl@WRFjfz|OoO9myX39%_1BC|p+h@?CQu;qm{&9f%^DQHyEI=b6D@rH-k1$A(fj?n0 zrnhz1e=gsC0Rchz?_n|mvJ#>qO3Jh{qJ{FK7QOTcZP<74uiucW9mP-6amghJes>ow zelK8AwU#m=Av@nxlfc$Y6NKZTZXN3|F(LbHJ`M5Vw-;HzAr`0fR- z_N`+aj}|s5p%(Ki-KOKzj4LDIeZWYasLqD^IeBoA5IJdyL8%UQR2%}{n$^Ag+2ewb zQRc_>?+Jls1*a&#S~0P9)8RCE@Hx{4J#CkppJ+)26c>E;W*@&^<0)JXs`Y+X?60|; 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HcmV?d00001 diff --git a/pom.xml b/pom.xml index 5c159fb985e50..4d88dd800f976 100644 --- a/pom.xml +++ b/pom.xml @@ -78,11 +78,6 @@ ${hbase.version} provided - - com.databricks - spark-avro_2.11 - 3.2.0 - com.hortonworks shc-core diff --git a/src/main/scala/com/yahoo/spark/starter/README.md b/src/main/scala/com/yahoo/spark/starter/README.md index 054293f8c5e7a..95a3322c7212a 100644 --- a/src/main/scala/com/yahoo/spark/starter/README.md +++ b/src/main/scala/com/yahoo/spark/starter/README.md @@ -62,13 +62,14 @@ $SPARK_HOME/bin/spark-submit --master yarn --deploy-mode cluster --conf "spark.h * Launching in client mode ```bash # launch the shell -$SPARK_HOME/bin/spark-submit --master yarn --deploy-mode client --conf "spark.hadoop.validateOutputSpecs=false" --jars $HBASE_JARS/hbase-protocol.jar,$HBASE_JARS/hbase-common.jar,$HBASE_JARS/hbase-client.jar,$HBASE_JARS/htrace-core-3.1.0-incubating.jar,$HBASE_JARS/hbase-server.jar,$HBASE_JARS/guava-12.0.1.jar,$HBASE_JARS/metrics-core-2.2.0.jar .... +$SPARK_HOME/bin/spark-shell --master yarn --deploy-mode client --conf "spark.hadoop.validateOutputSpecs=false" --jars $HBASE_JARS/hbase-protocol.jar,$HBASE_JARS/hbase-common.jar,$HBASE_JARS/hbase-client.jar,$HBASE_JARS/htrace-core-3.1.0-incubating.jar,$HBASE_JARS/hbase-server.jar,$HBASE_JARS/guava-12.0.1.jar,$HBASE_JARS/metrics-core-2.2.0.jar,shc-core-1.1.2-2.2-s_2.11-SNAPSHOT.jar ``` * If you plan to write to HBase using the connector, set the required conf `"spark.hadoop.validateOutputSpecs=false"`. This is to workaround a bug in HBase. See YHBASE-2131 for more details. ### Using the connector -You need to define a catalog so that a DataFrame can be mapped to an HBase table or vice-versa. This is defined in a json format. Below is an example for your reference explaining how to define your own catalog. +Check the hortonworks documentation for more details and examples [here](https://github.com/hortonworks-spark/shc) for further details. +Below is an additional example for your reference explaining how to define your own catalog.You need to define a catalog so that a DataFrame can be mapped to an HBase table or vice-versa. This is defined in a json format. ``` def catalog = s"""{ |"table":{"namespace":"default", "name":"table1"}, @@ -91,9 +92,6 @@ def catalog = s"""{ * `columns` field, you specify the name of the column in the DataFrame and its corresponding column_family, column_name and column_type in hbase. You identify the column/s which are used to form the rowkey using the column_family as rowkey. Note: Since a user can add any arbitrary columns to a column family, individual columns are not specified under the table schema in HBase table description. Therefore it is helpful to know the columns of interest available in hbase table while defining the catalog. - -For more examples, you can check the examples in the original repo [here](https://github.com/hortonworks-spark/shc). - diff --git a/src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala b/src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala index 506d51a359453..14da9e0ce7fc0 100644 --- a/src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala +++ b/src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala @@ -7,7 +7,9 @@ import org.apache.spark.sql.SparkSession case class DataRow(name: String, pet: String, horcruxes: Int, original_name: String) -// Simple example of accessing HBase from Spark using the Spark HBase connector +// Simple example of accessing HBase from Spark using the Spark HBase connector. +// The example creates an HBase table and writes sample data to it using the connector +// and reads back data from the same table and displays it to the stdout. object SparkClusterHBaseConnector { def main(args: Array[String]) { @@ -25,15 +27,16 @@ object SparkClusterHBaseConnector { import spark.implicits._ - val tableName = s"spark_test:${args(0)}" + val tableName = s"${args(0)}" - val df = Seq( DataRow("harry", "hedwig", 0, null), DataRow("voldy", "nagini", 7, "Tom")).toDF("name", "pet", "horcruxes", "original_name") + val df = Seq( DataRow("harry", "hedwig", 0, null), DataRow("voldy", "nagini", 7, "Tom")) + .toDF("name", "pet", "horcruxes", "original_name") // print the original dataframe df.show // define the catalog def catalog = s"""{ - |"table":{"namespace":"spark_test", "name":"harrypotter"}, + |"table":{"namespace":"spark_test", "name":"${tableName}"}, |"rowkey":"name", |"columns":{ |"name":{"cf":"rowkey", "col":"name", "type":"string"}, From d94ca321e19eceb1ef3dc5267b825965658c94bf Mon Sep 17 00:00:00 2001 From: Dhruve Ashar Date: Mon, 2 Jul 2018 11:43:28 -0500 Subject: [PATCH 3/3] Update pom.xml and make namespace configurable --- pom.xml | 2 +- .../yahoo/spark/starter/SparkClusterHBase.scala | 16 +++++++++------- .../starter/SparkClusterHBaseConnector.scala | 9 +++++---- 3 files changed, 15 insertions(+), 12 deletions(-) diff --git a/pom.xml b/pom.xml index 4d88dd800f976..413eaf1756beb 100644 --- a/pom.xml +++ b/pom.xml @@ -13,7 +13,7 @@ 2.11 2.11.8 1.7 - 0.98.7.19.1608152337_h2 + 1.3.2.13.1806282333_h2 diff --git a/src/main/scala/com/yahoo/spark/starter/SparkClusterHBase.scala b/src/main/scala/com/yahoo/spark/starter/SparkClusterHBase.scala index b8cab1a525137..61209491e6aba 100644 --- a/src/main/scala/com/yahoo/spark/starter/SparkClusterHBase.scala +++ b/src/main/scala/com/yahoo/spark/starter/SparkClusterHBase.scala @@ -13,8 +13,8 @@ object SparkClusterHBase { def main(args: Array[String]) { - if (args == null || args.length < 1) { - System.err.println("Usage: SparkClusterHBase ") + if (args == null || args.length < 2) { + System.err.println("Usage: SparkClusterHBase ") System.exit(1) } @@ -25,19 +25,21 @@ object SparkClusterHBase { getOrCreate() val hconf = HBaseConfiguration.create() - val tableName = s"spark_test:${args(0)}" - hconf.set(TableInputFormat.INPUT_TABLE, tableName) + val nameSpace = args(0) + val tableName = args(1) + val qualifiedTableName = nameSpace + ":" + tableName + hconf.set(TableInputFormat.INPUT_TABLE, qualifiedTableName) val admin = new HBaseAdmin(hconf) // create the table if not existed - if(!admin.isTableAvailable(tableName)) { - val tableDesc = new HTableDescriptor(tableName) + if(!admin.isTableAvailable(qualifiedTableName)) { + val tableDesc = new HTableDescriptor(qualifiedTableName) tableDesc.addFamily(new HColumnDescriptor("cf1".getBytes())); admin.createTable(tableDesc) } // put data into the table - val myTable = new HTable(hconf, tableName); + val myTable = new HTable(hconf, qualifiedTableName); for (i <- 0 to 5) { val p = new Put(new String("row" + i).getBytes()); p.add("cf1".getBytes(), "column-1".getBytes(), new String("value " + i).getBytes()); diff --git a/src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala b/src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala index 14da9e0ce7fc0..0d2492dfe717a 100644 --- a/src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala +++ b/src/main/scala/com/yahoo/spark/starter/SparkClusterHBaseConnector.scala @@ -14,8 +14,8 @@ object SparkClusterHBaseConnector { def main(args: Array[String]) { - if (args == null || args.length < 1) { - System.err.println("Usage: SparkClusterHBaseConnector ") + if (args == null || args.length < 2) { + System.err.println("Usage: SparkClusterHBaseConnector ") System.exit(1) } @@ -27,7 +27,8 @@ object SparkClusterHBaseConnector { import spark.implicits._ - val tableName = s"${args(0)}" + val nameSpace = s"${args(0)}" + val tableName = s"${args(1)}" val df = Seq( DataRow("harry", "hedwig", 0, null), DataRow("voldy", "nagini", 7, "Tom")) .toDF("name", "pet", "horcruxes", "original_name") @@ -36,7 +37,7 @@ object SparkClusterHBaseConnector { // define the catalog def catalog = s"""{ - |"table":{"namespace":"spark_test", "name":"${tableName}"}, + |"table":{"namespace":"${nameSpace}", "name":"${tableName}"}, |"rowkey":"name", |"columns":{ |"name":{"cf":"rowkey", "col":"name", "type":"string"},