简介
Spark SQL支持多种结构化数据源，轻松从各种数据源中读取Row对象。这些数据源包括Parquet、JSON、Hive表及关系型数据库等。
当只使用一部分字段时，Spark SQL可以智能地只扫描这些字段，而不会像hadoopFile方法一样简单粗暴地扫描全部数据。
Parquet
Parquet是一种流行的列式存储格式，可以高效地存储具有嵌套字段的记录。Parquet自动保存原始数据的类型，当写入Parquet文件时，所有的列会自动转为可空约束。
scala
// Encoders for most common types are automatically provided by importing spark.implicits._import spark.implicits._
val peopleDF = spark.read.json("examples/src/main/resources/people.json")// DataFrames can be saved as Parquet files, maintaining the schema informationpeopleDF.write.parquet("people.parquet")// Read in the parquet file created above// Parquet files are self-describing so the schema is preserved// The result of loading a Parquet file is also a DataFrameval parquetFileDF = spark.read.parquet("people.parquet")// Parquet files can also be used to create a temporary view and then used in SQL statementsparquetFileDF.createOrReplaceTempView("parquetFile")
val namesDF = spark.sql("SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19")
namesDF.map(attributes => "Name: " + attributes(0)).show()// +------------+// | value|// +------------+// |Name: Justin|// +------------+java
import org.apache.spark.api.java.function.MapFunction;import org.apache.spark.sql.Encoders;import org.apache.spark.sql.Dataset;import org.apache.spark.sql.Row;
Dataset<Row> peopleDF = spark.read().json("examples/src/main/resources/people.json");// DataFrames can be saved as Parquet files, maintaining the schema informationpeopleDF.write().parquet("people.parquet");// Read in the Parquet file created above.// Parquet files are self-describing so the schema is preserved// The result of loading a parquet file is also a DataFrameDataset<Row> parquetFileDF = spark.read().parquet("people.parquet");// Parquet files can also be used to create a temporary view and then used in SQL statementsparquetFileDF.createOrReplaceTempView("parquetFile");
Dataset<Row> namesDF = spark.sql("SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19");
Dataset<String> namesDS = namesDF.map(
(MapFunction<Row, String>) row -> "Name: " + row.getString(0),
Encoders.STRING());
namesDS.show();// +------------+// | value|// +------------+// |Name: Justin|// +------------+python
peopleDF = spark.read.json("examples/src/main/resources/people.json")# DataFrames can be saved as Parquet files, maintaining the schema information.peopleDF.write.parquet("people.parquet")# Read in the Parquet file created above.# Parquet files are self-describing so the schema is preserved.# The result of loading a parquet file is also a DataFrame.parquetFile = spark.read.parquet("people.parquet")# Parquet files can also be used to create a temporary view and then used in SQL statements.parquetFile.createOrReplaceTempView("parquetFile")
teenagers = spark.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
teenagers.show()# +------+# | name|# +------+# |Justin|# +------+sql
CREATE TEMPORARY VIEW parquetTable USING org.apache.spark.sql.parquet OPTIONS ( path "examples/src/main/resources/people.parquet") SELECT * FROM parquetTable
JSON
Spark SQL可以自动推断JSON数据集的结构，并加载为以Row为集合项的Dataset。
默认Spark SQL读取的json文件不是常规的json文件，每一行必须包含一个独立的、自包含的有效JSOn对象。对于常规的多行JSON文件，设置multiLine选项为true即可。
scala
// Primitive types (Int, String, etc) and Product types (case classes) encoders are// supported by importing this when creating a Dataset.import spark.implicits._// A JSON dataset is pointed to by path.// The path can be either a single text file or a directory storing text filesval path = "examples/src/main/resources/people.json"val peopleDF = spark.read.json(path)// The inferred schema can be visualized using the printSchema() methodpeopleDF.printSchema()// root// |-- age: long (nullable = true)// |-- name: string (nullable = true)// Creates a temporary view using the DataFramepeopleDF.createOrReplaceTempView("people")// SQL statements can be run by using the sql methods provided by sparkval teenagerNamesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19")
teenagerNamesDF.show()// +------+// | name|// +------+// |Justin|// +------+// Alternatively, a DataFrame can be created for a JSON dataset represented by// a Dataset[String] storing one JSON object per stringval otherPeopleDataset = spark.createDataset( """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
val otherPeople = spark.read.json(otherPeopleDataset)
otherPeople.show()// +---------------+----+// | address|name|// +---------------+----+// |[Columbus,Ohio]| Yin|// +---------------+----+java
import org.apache.spark.sql.Dataset;import org.apache.spark.sql.Row;// A JSON dataset is pointed to by path.// The path can be either a single text file or a directory storing text filesDataset<Row> people = spark.read().json("examples/src/main/resources/people.json");// The inferred schema can be visualized using the printSchema() methodpeople.printSchema();// root// |-- age: long (nullable = true)// |-- name: string (nullable = true)// Creates a temporary view using the DataFramepeople.createOrReplaceTempView("people");// SQL statements can be run by using the sql methods provided by sparkDataset<Row> namesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19");
namesDF.show();// +------+// | name|// +------+// |Justin|// +------+// Alternatively, a DataFrame can be created for a JSON dataset represented by// a Dataset<String> storing one JSON object per string.List<String> jsonData = Arrays.asList( "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
Dataset<String> anotherPeopleDataset = spark.createDataset(jsonData, Encoders.STRING());
Dataset<Row> anotherPeople = spark.read().json(anotherPeopleDataset);
anotherPeople.show();// +---------------+----+// | address|name|// +---------------+----+// |[Columbus,Ohio]| Yin|// +---------------+----+python
# spark is from the previous example.sc = spark.sparkContext# A JSON dataset is pointed to by path.# The path can be either a single text file or a directory storing text filespath = "examples/src/main/resources/people.json"peopleDF = spark.read.json(path)# The inferred schema can be visualized using the printSchema() methodpeopleDF.printSchema()# root# |-- age: long (nullable = true)# |-- name: string (nullable = true)# Creates a temporary view using the DataFramepeopleDF.createOrReplaceTempView("people")# SQL statements can be run by using the sql methods provided by sparkteenagerNamesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19")
teenagerNamesDF.show()# +------+# | name|# +------+# |Justin|# +------+# Alternatively, a DataFrame can be created for a JSON dataset represented by# an RDD[String] storing one JSON object per stringjsonStrings = ['{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}']
otherPeopleRDD = sc.parallelize(jsonStrings)
otherPeople = spark.read.json(otherPeopleRDD)
otherPeople.show()# +---------------+----+# | address|name|# +---------------+----+# |[Columbus,Ohio]| Yin|# +---------------+----+sql
CREATE TEMPORARY VIEW jsonTable USING org.apache.spark.sql.json OPTIONS ( path "examples/src/main/resources/people.json") SELECT * FROM jsonTable
Hive
Spark SQL支持任何Hive支持的存储格式（SerDe），包括文本文件、RCFiles、ORC、Parquet、Avro及Protocol Buffer等。
如果已配置好hive环境，将hive-site.xml，core-site.xml（用于安全配置），hdfs-site.xml（HDFS配置）放到conf目录下；如果没有hive环境，Spark SQL会自动在spark-warehouse（spark.sql.warehouse.dir配置项）目录下创建metastore_db。另外，需要赋予执行spark应用的用户写权限。
scala
import java.io.Fileimport org.apache.spark.sql.Rowimport org.apache.spark.sql.SparkSessioncase class Record(key: Int, value: String)// warehouseLocation points to the default location for managed databases and tablesval warehouseLocation = new File("spark-warehouse").getAbsolutePath
val spark = SparkSession
.builder()
.appName("Spark Hive Example")
.config("spark.sql.warehouse.dir", warehouseLocation)
.enableHiveSupport()
.getOrCreate()import spark.implicits._import spark.sql
sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")
sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")// Queries are expressed in HiveQLsql("SELECT * FROM src").show()// +---+-------+// |key| value|// +---+-------+// |238|val_238|// | 86| val_86|// |311|val_311|// ...// Aggregation queries are also supported.sql("SELECT COUNT(*) FROM src").show()// +--------+// |count(1)|// +--------+// | 500 |// +--------+// The results of SQL queries are themselves DataFrames and support all normal functions.val sqlDF = sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")// The items in DataFrames are of type Row, which allows you to access each column by ordinal.val stringsDS = sqlDF.map { case Row(key: Int, value: String) => s"Key: $key, Value: $value"}
stringsDS.show()// +--------------------+// | value|// +--------------------+// |Key: 0, Value: val_0|// |Key: 0, Value: val_0|// |Key: 0, Value: val_0|// ...// You can also use DataFrames to create temporary views within a SparkSession.val recordsDF = spark.createDataFrame((1 to 100).map(i => Record(i, s"val_$i")))
recordsDF.createOrReplaceTempView("records")// Queries can then join DataFrame data with data stored in Hive.sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show()// +---+------+---+------+// |key| value|key| value|// +---+------+---+------+// | 2| val_2| 2| val_2|// | 4| val_4| 4| val_4|// | 5| val_5| 5| val_5|// ...java
import java.io.File;import java.io.Serializable;import java.util.ArrayList;import java.util.List;import org.apache.spark.api.java.function.MapFunction;import org.apache.spark.sql.Dataset;import org.apache.spark.sql.Encoders;import org.apache.spark.sql.Row;import org.apache.spark.sql.SparkSession;public static class Record implements Serializable { private int key; private String value; public int getKey() { return key;
} public void setKey(int key) { this.key = key;
} public String getValue() { return value;
} public void setValue(String value) { this.value = value;
}
}// warehouseLocation points to the default location for managed databases and tablesString warehouseLocation = new File("spark-warehouse").getAbsolutePath();
SparkSession spark = SparkSession
.builder()
.appName("Java Spark Hive Example")
.config("spark.sql.warehouse.dir", warehouseLocation)
.enableHiveSupport()
.getOrCreate();
spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive");
spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src");// Queries are expressed in HiveQLspark.sql("SELECT * FROM src").show();// +---+-------+// |key| value|// +---+-------+// |238|val_238|// | 86| val_86|// |311|val_311|// ...// Aggregation queries are also supported.spark.sql("SELECT COUNT(*) FROM src").show();// +--------+// |count(1)|// +--------+// | 500 |// +--------+// The results of SQL queries are themselves DataFrames and support all normal functions.Dataset<Row> sqlDF = spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key");// The items in DataFrames are of type Row, which lets you to access each column by ordinal.Dataset<String> stringsDS = sqlDF.map(
(MapFunction<Row, String>) row -> "Key: " + row.get(0) + ", Value: " + row.get(1),
Encoders.STRING());
stringsDS.show();// +--------------------+// | value|// +--------------------+// |Key: 0, Value: val_0|// |Key: 0, Value: val_0|// |Key: 0, Value: val_0|// ...// You can also use DataFrames to create temporary views within a SparkSession.List<Record> records = new ArrayList<>();for (int key = 1; key < 100; key++) {
Record record = new Record();
record.setKey(key);
record.setValue("val_" + key);
records.add(record);
}
Dataset<Row> recordsDF = spark.createDataFrame(records, Record.class);
recordsDF.createOrReplaceTempView("records");// Queries can then join DataFrames data with data stored in Hive.spark.sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show();// +---+------+---+------+// |key| value|key| value|// +---+------+---+------+// | 2| val_2| 2| val_2|// | 2| val_2| 2| val_2|// | 4| val_4| 4| val_4|// ...python
from os.path import expanduser, join, abspathfrom pyspark.sql import SparkSessionfrom pyspark.sql import Row# warehouse_location points to the default location for managed databases and tableswarehouse_location = abspath('spark-warehouse')
spark = SparkSession \
.builder \
.appName("Python Spark SQL Hive integration example") \
.config("spark.sql.warehouse.dir", warehouse_location) \
.enableHiveSupport() \
.getOrCreate()# spark is an existing SparkSessionspark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")
spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")# Queries are expressed in HiveQLspark.sql("SELECT * FROM src").show()# +---+-------+# |key| value|# +---+-------+# |238|val_238|# | 86| val_86|# |311|val_311|# ...# Aggregation queries are also supported.spark.sql("SELECT COUNT(*) FROM src").show()# +--------+# |count(1)|# +--------+# | 500 |# +--------+# The results of SQL queries are themselves DataFrames and support all normal functions.sqlDF = spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")# The items in DataFrames are of type Row, which allows you to access each column by ordinal.stringsDS = sqlDF.rdd.map(lambda row: "Key: %d, Value: %s" % (row.key, row.value))for record in stringsDS.collect():
print(record)# Key: 0, Value: val_0# Key: 0, Value: val_0# Key: 0, Value: val_0# ...# You can also use DataFrames to create temporary views within a SparkSession.Record = Row("key", "value")
recordsDF = spark.createDataFrame([Record(i, "val_" + str(i)) for i in range(1, 101)])
recordsDF.createOrReplaceTempView("records")# Queries can then join DataFrame data with data stored in Hive.spark.sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show()# +---+------+---+------+# |key| value|key| value|# +---+------+---+------+# | 2| val_2| 2| val_2|# | 4| val_4| 4| val_4|# | 5| val_5| 5| val_5|# ...JDBC连接
Spark SQL可以使用JDBC连接读写关系型数据库中的数据。这种方式比使用spark core中的JdbcRDD要好，因为生成的DataFrame可以很容易被处理。
scala
// Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods// Loading data from a JDBC sourceval jdbcDF = spark.read
.format("jdbc")
.option("url", "jdbc:postgresql:dbserver")
.option("dbtable", "schema.tablename")
.option("user", "username")
.option("password", "password")
.load()
val connectionProperties = new Properties()
connectionProperties.put("user", "username")
connectionProperties.put("password", "password")
val jdbcDF2 = spark.read
.jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)// Saving data to a JDBC sourcejdbcDF.write
.format("jdbc")
.option("url", "jdbc:postgresql:dbserver")
.option("dbtable", "schema.tablename")
.option("user", "username")
.option("password", "password")
.save()
jdbcDF2.write
.jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)// Specifying create table column data types on writejdbcDF.write
.option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
.jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)java
// Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods// Loading data from a JDBC sourceDataset<Row> jdbcDF = spark.read()
.format("jdbc")
.option("url", "jdbc:postgresql:dbserver")
.option("dbtable", "schema.tablename")
.option("user", "username")
.option("password", "password")
.load();
Properties connectionProperties = new Properties();
connectionProperties.put("user", "username");
connectionProperties.put("password", "password");
Dataset<Row> jdbcDF2 = spark.read()
.jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);// Saving data to a JDBC sourcejdbcDF.write()
.format("jdbc")
.option("url", "jdbc:postgresql:dbserver")
.option("dbtable", "schema.tablename")
.option("user", "username")
.option("password", "password")
.save();
jdbcDF2.write()
.jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);// Specifying create table column data types on writejdbcDF.write()
.option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
.jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);python
# Note: JDBC loading and saving can be achieved via either the load/save or jdbc methods# Loading data from a JDBC sourcejdbcDF = spark.read \
.format("jdbc") \
.option("url", "jdbc:postgresql:dbserver") \
.option("dbtable", "schema.tablename") \
.option("user", "username") \
.option("password", "password") \
.load()
jdbcDF2 = spark.read \
.jdbc("jdbc:postgresql:dbserver", "schema.tablename",
properties={"user": "username", "password": "password"})# Saving data to a JDBC sourcejdbcDF.write \
.format("jdbc") \
.option("url", "jdbc:postgresql:dbserver") \
.option("dbtable", "schema.tablename") \
.option("user", "username") \
.option("password", "password") \
.save()
jdbcDF2.write \
.jdbc("jdbc:postgresql:dbserver", "schema.tablename",
properties={"user": "username", "password": "password"})# Specifying create table column data types on writejdbcDF.write \
.option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)") \
.jdbc("jdbc:postgresql:dbserver", "schema.tablename",
properties={"user": "username", "password": "password"})sql
CREATE TEMPORARY VIEW jdbcTable USING org.apache.spark.sql.jdbc OPTIONS ( url "jdbc:postgresql:dbserver", dbtable "schema.tablename", user 'username', password 'password') INSERT INTO TABLE jdbcTable SELECT * FROM resultTable
作者：java大数据编程
链接：https://www.jianshu.com/p/7e3ab68d3c2f
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