如何查询avro格式的数据集？
How to query datasets in avro format?
这适用于镶木地板
val sqlDF = spark.sql("SELECT DISTINCT field FROM parquet.`file-path'")
我在 Avro 上尝试了同样的方法，但即使我使用 com.databricks.spark.avro.
，它仍然给我一个错误
当我执行以下查询时：
val sqlDF = spark.sql("SELECT DISTINCT Source_Product_Classification FROM avro.`file path`")
我得到 AnalysisException。为什么？
org.apache.spark.sql.AnalysisException: Failed to find data source: avro. Please find an Avro package at http://spark.apache.org/third-party-projects.html;; line 1 pos 51
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.execution.datasources.ResolveDataSource$$anonfun$apply.applyOrElse(rules.scala:61)
at org.apache.spark.sql.execution.datasources.ResolveDataSource$$anonfun$apply.applyOrElse(rules.scala:38)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators.apply(LogicalPlan.scala:61)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators.apply(LogicalPlan.scala:61)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:60)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun.apply(LogicalPlan.scala:58)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun.apply(LogicalPlan.scala:58)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun.apply(TreeNode.scala:307)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:58)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun.apply(LogicalPlan.scala:58)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun.apply(LogicalPlan.scala:58)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun.apply(TreeNode.scala:307)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:58)
at org.apache.spark.sql.execution.datasources.ResolveDataSource.apply(rules.scala:38)
at org.apache.spark.sql.execution.datasources.ResolveDataSource.apply(rules.scala:37)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$$anonfun$apply.apply(RuleExecutor.scala:85)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$$anonfun$apply.apply(RuleExecutor.scala:82)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute.apply(RuleExecutor.scala:82)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute.apply(RuleExecutor.scala:74)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:69)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:67)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:592)
将格式名称更改为 com.databricks.spark.avro 没有任何区别，查询失败。
val sqlDF = spark.sql("SELECT DISTINCT Source_Product_Classification FROM com.databricks.spark.avro`file-path`")
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input '.' expecting {<EOF>, ',', 'SELECT', 'FROM', 'ADD', 'AS', 'ALL', 'DISTINCT', 'WHERE', 'GROUP', 'BY', 'GROUPING', 'SETS', 'CUBE', 'ROLLUP', 'ORDER', 'HAVING', 'LIMIT', 'AT', 'OR', 'AND', 'IN', NOT, 'NO', 'EXISTS', 'BETWEEN', 'LIKE', RLIKE, 'IS', 'NULL', 'TRUE', 'FALSE', 'NULLS', 'ASC', 'DESC', 'FOR', 'INTERVAL', 'CASE', 'WHEN', 'THEN', 'ELSE', 'END', 'JOIN', 'CROSS', 'OUTER', 'INNER', 'LEFT', 'RIGHT', 'FULL', 'NATURAL', 'LATERAL', 'WINDOW', 'OVER', 'PARTITION', 'RANGE', 'ROWS', 'UNBOUNDED', 'PRECEDING', 'FOLLOWING', 'CURRENT', 'FIRST', 'LAST', 'ROW', 'WITH', 'VALUES', 'CREATE', 'TABLE', 'VIEW', 'REPLACE', 'INSERT', 'DELETE', 'INTO', 'DESCRIBE', 'EXPLAIN', 'FORMAT', 'LOGICAL', 'CODEGEN', 'CAST', 'SHOW', 'TABLES', 'COLUMNS', 'COLUMN', 'USE', 'PARTITIONS', 'FUNCTIONS', 'DROP', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', 'TO', 'TABLESAMPLE', 'STRATIFY', 'ALTER', 'RENAME', 'ARRAY', 'MAP', 'STRUCT', 'COMMENT', 'SET', 'RESET', 'DATA', 'START', 'TRANSACTION', 'COMMIT', 'ROLLBACK', 'MACRO', 'IF', 'DIV', 'PERCENT', 'BUCKET', 'OUT', 'OF', 'SORT', 'CLUSTER', 'DISTRIBUTE', 'OVERWRITE', 'TRANSFORM', 'REDUCE', 'USING', 'SERDE', 'SERDEPROPERTIES', 'RECORDREADER', 'RECORDWRITER', 'DELIMITED', 'FIELDS', 'TERMINATED', 'COLLECTION', 'ITEMS', 'KEYS', 'ESCAPED', 'LINES', 'SEPARATED', 'FUNCTION', 'EXTENDED', 'REFRESH', 'CLEAR', 'CACHE', 'UNCACHE', 'LAZY', 'FORMATTED', 'GLOBAL', TEMPORARY, 'OPTIONS', 'UNSET', 'TBLPROPERTIES', 'DBPROPERTIES', 'BUCKETS', 'SKEWED', 'STORED', 'DIRECTORIES', 'LOCATION', 'EXCHANGE', 'ARCHIVE', 'UNARCHIVE', 'FILEFORMAT', 'TOUCH', 'COMPACT', 'CONCATENATE', 'CHANGE', 'CASCADE', 'RESTRICT', 'CLUSTERED', 'SORTED', 'PURGE', 'INPUTFORMAT', 'OUTPUTFORMAT', DATABASE, DATABASES, 'DFS', 'TRUNCATE', 'ANALYZE', 'COMPUTE', 'LIST', 'STATISTICS', 'PARTITIONED', 'EXTERNAL', 'DEFINED', 'REVOKE', 'GRANT', 'LOCK', 'UNLOCK', 'MSCK', 'REPAIR', 'RECOVER', 'EXPORT', 'IMPORT', 'LOAD', 'ROLE', 'ROLES', 'COMPACTIONS', 'PRINCIPALS', 'TRANSACTIONS', 'INDEX', 'INDEXES', 'LOCKS', 'OPTION', 'ANTI', 'LOCAL', 'INPATH', 'CURRENT_DATE', 'CURRENT_TIMESTAMP', IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 65)
== SQL ==
SELECT DISTINCT Source_Product_Classification FROM com.databricks.spark.avro`/uat/myfile`
-----------------------------------------------------------------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:197)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:99)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:45)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:53)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:592)
... 48 elided
Spark SQL 通过单独的 spark-avro 模块支持 avro 格式。
A library for reading and writing Avro data from Spark SQL.
请注意，spark-avro 是一个单独的模块，默认情况下不包含在 Spark 中。
您应该使用 spark-submit --packages 加载模块，例如
$ bin/spark-shell --packages com.databricks:spark-avro_2.11:3.2.0
Jaceks 的回答总体上有效，但在我的环境中，由于不明原因，它无法正常工作。 spark-shell --packages com.databricks:spark-avro_2.11:3.2.0 挂了很长时间没有产生任何结果。
我使用 --jars 选项和 spark-shell
解决了这个问题
步骤：
1) 转到 https://mvnrepository.com/artifact/com.databricks/spark-avro_2.11/4.0.0
复制 link jar http://central.maven.org/maven2/com/databricks/spark-avro_2.11/4.0.0/spark-avro_2.11-4.0.0.jar
的地址
2) wget http://central.maven.org/maven2/com/databricks/spark-avro_2.11/4.0.0/spark-avro_2.11-4.0.0.jar。
3) spark-shell --jars <pathwhere you downloaded jar file>/spark-avro_2.11-4.0.0.jar
4)spark.read.format("com.databricks.spark.avro").load("s3://MYAVROLOCATION.avro")
已转换为数据帧并能够打印它。
在你的情况下，一旦你获得了数据框，你就可以sql在你的路上。
注意： 如果你没有使用 spark-shell 你可以使用 sbt 或 maven spark-avro_2.11-4.0.0.jar 使用以下 Maven 坐标。
<dependency>
<groupId>com.databricks</groupId>
<artifactId>spark-avro_2.11</artifactId>
<version>4.0.0</version>
</dependency>
Note : Avro datasource was introduced in spark 2.4 on wards.. SparkSPARK-24768
Have a built-in AVRO data source implementation
Which means that all the above things are not necessary any more.
See spark-release-2-4-0 release notes
Spark Avro 集成：
通过使用 Spark，我们可以使用 spark-avro 模块集成 avro 格式。 spark-avro 库最初由 databricks 作为开源库开发。 spark-avro 模块是外部模块，不包含在 spark-submit 或 spark-shell[=86 中=] 默认情况下。所以在外部我们需要在提交spark作业时指定。
下一节，我将讲解如何整合Spark和Avro数据格式。
Spark 版本 > 2.4
从 Spark 2.4 版本开始，Spark SQL 提供了对读取和写入 Apache Avro 数据的内置支持。
Maven 依赖：
https://mvnrepository.com/artifact/org.apache.spark/spark-avro
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-avro_2.12</artifactId>
<version>2.4.5</version>
</dependency>
Spark 提交：
./bin/spark-submit --packages org.apache.spark:spark-avro_2.12:2.4.5 ...
SparkShell：
./bin/spark-shell --packages org.apache.spark:spark-avro_2.12:2.4.5 ...
示例：
SparkAvroWriteExample.scala
import org.apache.spark.SparkConf;
import org.apache.spark.sql.SparkSession;
case class Employee( id:Long, name:String, salary:Float, deptId: Int)
object SparkAvroWriteExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setIfMissing("spark.master", "local[*]").setAppName("Spark Avro Read Examples")
val spark = SparkSession.builder().config(conf).getOrCreate();
val employeeList = List(Employee(1, "Ranga", 10000, 1),
Employee(2, "Vinod", 1000, 1),
Employee(3, "Nishanth", 500000, 2),
Employee(4, "Manoj", 25000, 1),
Employee(5, "Yashu", 1600, 1),
Employee(6, "Raja", 50000, 2)
);
val employeeDF = spark.createDataFrame(employeeList);
employeeDF.coalesce(1).write.format("avro").mode("overwrite").save("employees.avro");
spark.close();
}
}
SparkAvroReadExample.scala
import org.apache.spark.SparkConf;
import org.apache.spark.sql.SparkSession;
object SparkAvroReadExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setIfMissing("spark.master", "local[*]").setAppName("Spark Avro Read Examples")
val spark = SparkSession.builder().config(conf).getOrCreate();
val employeeDF = spark.read.format("avro").load("employees.avro");
employeeDF.printSchema();
employeeDF.foreach(employee => {println(employee);});
spark.close();
}
}
Github link
https://github.com/rangareddy/ranga-spark-poc/tree/master/spark-2.4/SparkAvro
Spark 版本 < 2.4
在 Spark 版本 < 2.4 中，我们需要明确指定 avro 格式为 com.databricks.spark.avro 否则我们将得到 org.apache.spark.sql.AnalysisException: Failed to find data source: avro. 错误。
Maven 依赖：
Spark Version Compatible version of Avro Data Source for Spark
1.2 0.2.0
1.3 1.0.0
1.4+ 2.0.1
2.0 - 2.1 3.2.0
2.2 - 2.3 4.0.0
https://mvnrepository.com/artifact/com.databricks/spark-avro
<dependency>
<groupId>com.databricks</groupId>
<artifactId>spark-avro_2.11</artifactId>
<version>4.0.0</version>
</dependency>
Spark 提交：
./bin/spark-submit --packages com.databricks:spark-avro_2.11:4.0.0 ...
SparkShell：
./bin/spark-shell --packages com.databricks:spark-avro_2.11:4.0.0 ...
例子:
SparkAvroWriteExample.scala
import org.apache.spark.SparkConf;
import org.apache.spark.sql.SparkSession;
case class Employee( id:Long, name:String, salary:Float, deptId: Int)
object SparkAvroWriteExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setIfMissing("spark.master", "local[*]").setAppName("Spark Avro Read Examples")
val spark = SparkSession.builder().config(conf).getOrCreate();
val employeeList = List(Employee(1, "Ranga", 10000, 1),
Employee(2, "Vinod", 1000, 1),
Employee(3, "Nishanth", 500000, 2),
Employee(4, "Manoj", 25000, 1),
Employee(5, "Yashu", 1600, 1),
Employee(6, "Raja", 50000, 2)
);
val employeeDF = spark.createDataFrame(employeeList);
employeeDF.coalesce(1).write.format("com.databricks.spark.avro").mode("overwrite").save("employees.avro");
spark.close();
}
}
SparkAvroReadExample.scala
import org.apache.spark.SparkConf;
import org.apache.spark.sql.SparkSession;
object SparkAvroReadExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setIfMissing("spark.master", "local[*]").setAppName("Spark Avro Read Examples")
val spark = SparkSession.builder().config(conf).getOrCreate();
val employeeDF = spark.read.format("com.databricks.spark.avro").load("employees.avro");
employeeDF.printSchema();
employeeDF.foreach(employee => {println(employee);});
spark.close();
}
}
Github link
https://github.com/rangareddy/ranga-spark-poc/tree/master/spark-2.3/SparkAvro
这就是所有人！！
这适用于镶木地板
val sqlDF = spark.sql("SELECT DISTINCT field FROM parquet.`file-path'")
我在 Avro 上尝试了同样的方法，但即使我使用 com.databricks.spark.avro.
当我执行以下查询时：
val sqlDF = spark.sql("SELECT DISTINCT Source_Product_Classification FROM avro.`file path`")
我得到 AnalysisException。为什么？
org.apache.spark.sql.AnalysisException: Failed to find data source: avro. Please find an Avro package at http://spark.apache.org/third-party-projects.html;; line 1 pos 51
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.execution.datasources.ResolveDataSource$$anonfun$apply.applyOrElse(rules.scala:61)
at org.apache.spark.sql.execution.datasources.ResolveDataSource$$anonfun$apply.applyOrElse(rules.scala:38)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators.apply(LogicalPlan.scala:61)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators.apply(LogicalPlan.scala:61)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:60)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun.apply(LogicalPlan.scala:58)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun.apply(LogicalPlan.scala:58)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun.apply(TreeNode.scala:307)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:58)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun.apply(LogicalPlan.scala:58)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun.apply(LogicalPlan.scala:58)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun.apply(TreeNode.scala:307)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:58)
at org.apache.spark.sql.execution.datasources.ResolveDataSource.apply(rules.scala:38)
at org.apache.spark.sql.execution.datasources.ResolveDataSource.apply(rules.scala:37)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$$anonfun$apply.apply(RuleExecutor.scala:85)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$$anonfun$apply.apply(RuleExecutor.scala:82)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute.apply(RuleExecutor.scala:82)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute.apply(RuleExecutor.scala:74)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:69)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:67)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:592)
将格式名称更改为 com.databricks.spark.avro 没有任何区别，查询失败。
val sqlDF = spark.sql("SELECT DISTINCT Source_Product_Classification FROM com.databricks.spark.avro`file-path`")
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input '.' expecting {<EOF>, ',', 'SELECT', 'FROM', 'ADD', 'AS', 'ALL', 'DISTINCT', 'WHERE', 'GROUP', 'BY', 'GROUPING', 'SETS', 'CUBE', 'ROLLUP', 'ORDER', 'HAVING', 'LIMIT', 'AT', 'OR', 'AND', 'IN', NOT, 'NO', 'EXISTS', 'BETWEEN', 'LIKE', RLIKE, 'IS', 'NULL', 'TRUE', 'FALSE', 'NULLS', 'ASC', 'DESC', 'FOR', 'INTERVAL', 'CASE', 'WHEN', 'THEN', 'ELSE', 'END', 'JOIN', 'CROSS', 'OUTER', 'INNER', 'LEFT', 'RIGHT', 'FULL', 'NATURAL', 'LATERAL', 'WINDOW', 'OVER', 'PARTITION', 'RANGE', 'ROWS', 'UNBOUNDED', 'PRECEDING', 'FOLLOWING', 'CURRENT', 'FIRST', 'LAST', 'ROW', 'WITH', 'VALUES', 'CREATE', 'TABLE', 'VIEW', 'REPLACE', 'INSERT', 'DELETE', 'INTO', 'DESCRIBE', 'EXPLAIN', 'FORMAT', 'LOGICAL', 'CODEGEN', 'CAST', 'SHOW', 'TABLES', 'COLUMNS', 'COLUMN', 'USE', 'PARTITIONS', 'FUNCTIONS', 'DROP', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', 'TO', 'TABLESAMPLE', 'STRATIFY', 'ALTER', 'RENAME', 'ARRAY', 'MAP', 'STRUCT', 'COMMENT', 'SET', 'RESET', 'DATA', 'START', 'TRANSACTION', 'COMMIT', 'ROLLBACK', 'MACRO', 'IF', 'DIV', 'PERCENT', 'BUCKET', 'OUT', 'OF', 'SORT', 'CLUSTER', 'DISTRIBUTE', 'OVERWRITE', 'TRANSFORM', 'REDUCE', 'USING', 'SERDE', 'SERDEPROPERTIES', 'RECORDREADER', 'RECORDWRITER', 'DELIMITED', 'FIELDS', 'TERMINATED', 'COLLECTION', 'ITEMS', 'KEYS', 'ESCAPED', 'LINES', 'SEPARATED', 'FUNCTION', 'EXTENDED', 'REFRESH', 'CLEAR', 'CACHE', 'UNCACHE', 'LAZY', 'FORMATTED', 'GLOBAL', TEMPORARY, 'OPTIONS', 'UNSET', 'TBLPROPERTIES', 'DBPROPERTIES', 'BUCKETS', 'SKEWED', 'STORED', 'DIRECTORIES', 'LOCATION', 'EXCHANGE', 'ARCHIVE', 'UNARCHIVE', 'FILEFORMAT', 'TOUCH', 'COMPACT', 'CONCATENATE', 'CHANGE', 'CASCADE', 'RESTRICT', 'CLUSTERED', 'SORTED', 'PURGE', 'INPUTFORMAT', 'OUTPUTFORMAT', DATABASE, DATABASES, 'DFS', 'TRUNCATE', 'ANALYZE', 'COMPUTE', 'LIST', 'STATISTICS', 'PARTITIONED', 'EXTERNAL', 'DEFINED', 'REVOKE', 'GRANT', 'LOCK', 'UNLOCK', 'MSCK', 'REPAIR', 'RECOVER', 'EXPORT', 'IMPORT', 'LOAD', 'ROLE', 'ROLES', 'COMPACTIONS', 'PRINCIPALS', 'TRANSACTIONS', 'INDEX', 'INDEXES', 'LOCKS', 'OPTION', 'ANTI', 'LOCAL', 'INPATH', 'CURRENT_DATE', 'CURRENT_TIMESTAMP', IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 65)
== SQL ==
SELECT DISTINCT Source_Product_Classification FROM com.databricks.spark.avro`/uat/myfile`
-----------------------------------------------------------------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:197)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:99)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:45)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:53)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:592)
... 48 elided
Spark SQL 通过单独的 spark-avro 模块支持 avro 格式。
A library for reading and writing Avro data from Spark SQL.
请注意，spark-avro 是一个单独的模块，默认情况下不包含在 Spark 中。
您应该使用 spark-submit --packages 加载模块，例如
$ bin/spark-shell --packages com.databricks:spark-avro_2.11:3.2.0
Jaceks 的回答总体上有效，但在我的环境中，由于不明原因，它无法正常工作。 spark-shell --packages com.databricks:spark-avro_2.11:3.2.0 挂了很长时间没有产生任何结果。
我使用 --jars 选项和 spark-shell
步骤：
1) 转到 https://mvnrepository.com/artifact/com.databricks/spark-avro_2.11/4.0.0 复制 link jar http://central.maven.org/maven2/com/databricks/spark-avro_2.11/4.0.0/spark-avro_2.11-4.0.0.jar
的地址2) wget http://central.maven.org/maven2/com/databricks/spark-avro_2.11/4.0.0/spark-avro_2.11-4.0.0.jar。
3) spark-shell --jars <pathwhere you downloaded jar file>/spark-avro_2.11-4.0.0.jar
4)spark.read.format("com.databricks.spark.avro").load("s3://MYAVROLOCATION.avro")
已转换为数据帧并能够打印它。
在你的情况下，一旦你获得了数据框，你就可以sql在你的路上。
注意： 如果你没有使用 spark-shell 你可以使用 sbt 或 maven spark-avro_2.11-4.0.0.jar 使用以下 Maven 坐标。
<dependency>
<groupId>com.databricks</groupId>
<artifactId>spark-avro_2.11</artifactId>
<version>4.0.0</version>
</dependency>
Note : Avro datasource was introduced in spark 2.4 on wards.. SparkSPARK-24768 Have a built-in AVRO data source implementation
Which means that all the above things are not necessary any more. See spark-release-2-4-0 release notes
Spark Avro 集成： 通过使用 Spark，我们可以使用 spark-avro 模块集成 avro 格式。 spark-avro 库最初由 databricks 作为开源库开发。 spark-avro 模块是外部模块，不包含在 spark-submit 或 spark-shell[=86 中=] 默认情况下。所以在外部我们需要在提交spark作业时指定。
下一节，我将讲解如何整合Spark和Avro数据格式。
Spark 版本 > 2.4 从 Spark 2.4 版本开始，Spark SQL 提供了对读取和写入 Apache Avro 数据的内置支持。
Maven 依赖： https://mvnrepository.com/artifact/org.apache.spark/spark-avro
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-avro_2.12</artifactId>
<version>2.4.5</version>
</dependency>
Spark 提交：
./bin/spark-submit --packages org.apache.spark:spark-avro_2.12:2.4.5 ...
SparkShell：
./bin/spark-shell --packages org.apache.spark:spark-avro_2.12:2.4.5 ...
示例：
SparkAvroWriteExample.scala
import org.apache.spark.SparkConf;
import org.apache.spark.sql.SparkSession;
case class Employee( id:Long, name:String, salary:Float, deptId: Int)
object SparkAvroWriteExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setIfMissing("spark.master", "local[*]").setAppName("Spark Avro Read Examples")
val spark = SparkSession.builder().config(conf).getOrCreate();
val employeeList = List(Employee(1, "Ranga", 10000, 1),
Employee(2, "Vinod", 1000, 1),
Employee(3, "Nishanth", 500000, 2),
Employee(4, "Manoj", 25000, 1),
Employee(5, "Yashu", 1600, 1),
Employee(6, "Raja", 50000, 2)
);
val employeeDF = spark.createDataFrame(employeeList);
employeeDF.coalesce(1).write.format("avro").mode("overwrite").save("employees.avro");
spark.close();
}
}
SparkAvroReadExample.scala
import org.apache.spark.SparkConf;
import org.apache.spark.sql.SparkSession;
object SparkAvroReadExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setIfMissing("spark.master", "local[*]").setAppName("Spark Avro Read Examples")
val spark = SparkSession.builder().config(conf).getOrCreate();
val employeeDF = spark.read.format("avro").load("employees.avro");
employeeDF.printSchema();
employeeDF.foreach(employee => {println(employee);});
spark.close();
}
}
Github link https://github.com/rangareddy/ranga-spark-poc/tree/master/spark-2.4/SparkAvro
Spark 版本 < 2.4 在 Spark 版本 < 2.4 中，我们需要明确指定 avro 格式为 com.databricks.spark.avro 否则我们将得到 org.apache.spark.sql.AnalysisException: Failed to find data source: avro. 错误。
Maven 依赖：
Spark Version Compatible version of Avro Data Source for Spark
1.2 0.2.0
1.3 1.0.0
1.4+ 2.0.1
2.0 - 2.1 3.2.0
2.2 - 2.3 4.0.0
https://mvnrepository.com/artifact/com.databricks/spark-avro
<dependency>
<groupId>com.databricks</groupId>
<artifactId>spark-avro_2.11</artifactId>
<version>4.0.0</version>
</dependency>
Spark 提交：
./bin/spark-submit --packages com.databricks:spark-avro_2.11:4.0.0 ...
SparkShell：
./bin/spark-shell --packages com.databricks:spark-avro_2.11:4.0.0 ...
例子:
SparkAvroWriteExample.scala
import org.apache.spark.SparkConf;
import org.apache.spark.sql.SparkSession;
case class Employee( id:Long, name:String, salary:Float, deptId: Int)
object SparkAvroWriteExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setIfMissing("spark.master", "local[*]").setAppName("Spark Avro Read Examples")
val spark = SparkSession.builder().config(conf).getOrCreate();
val employeeList = List(Employee(1, "Ranga", 10000, 1),
Employee(2, "Vinod", 1000, 1),
Employee(3, "Nishanth", 500000, 2),
Employee(4, "Manoj", 25000, 1),
Employee(5, "Yashu", 1600, 1),
Employee(6, "Raja", 50000, 2)
);
val employeeDF = spark.createDataFrame(employeeList);
employeeDF.coalesce(1).write.format("com.databricks.spark.avro").mode("overwrite").save("employees.avro");
spark.close();
}
}
SparkAvroReadExample.scala
import org.apache.spark.SparkConf;
import org.apache.spark.sql.SparkSession;
object SparkAvroReadExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setIfMissing("spark.master", "local[*]").setAppName("Spark Avro Read Examples")
val spark = SparkSession.builder().config(conf).getOrCreate();
val employeeDF = spark.read.format("com.databricks.spark.avro").load("employees.avro");
employeeDF.printSchema();
employeeDF.foreach(employee => {println(employee);});
spark.close();
}
}
Github link https://github.com/rangareddy/ranga-spark-poc/tree/master/spark-2.3/SparkAvro
这就是所有人！！