Spark series (10) — Spark SQL external data source

A list,

1.1 Multi-data source support

Spark supports the following six core data sources. In addition, the Spark community provides hundreds of data source reading methods, which can meet most application scenarios.

• CSV
• JSON
• Parquet
• ORC
• JDBC/ODBC connections
• Plain-text files

Note: All of the following test files can be downloaded from the Resources directory of this repository

1.2 Read Data Format

All read apis follow the following call format:

/ / formatDataFrameReader.format(...) .option("key"."value").schema(...) .load()/ / sample
spark.read.format("csv")
.option("mode"."FAILFAST")          // Read mode
.option("inferSchema"."true")       // Whether to automatically infer schema
.option("path"."path/to/file(s)")   // File path
.schema(someSchema)                  // Use the predefined schema
.load()
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The read mode has the following three options:

Read mode	describe
permissive	When a corrupted record is encountered, all its fields are set to NULL and all corrupted records are placed in a string column named _corruption T_record
dropMalformed	Delete improperly formatted lines
failFast	Fail immediately when malformed data is encountered

1.3 Write Data Format

/ / formatDataFrameWriter.format(...) .option(...) .partitionBy(...) .bucketBy(...) .sortBy(...) .save()/ / sample
dataframe.write.format("csv")
.option("mode"."OVERWRITE")         / / write mode
.option("dateFormat"."yyyy-MM-dd")  // Date format
.option("path"."path/to/file(s)")
.save()
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The write data mode has the following four options:

Scala/Java	describe
SaveMode.ErrorIfExists	If a file already exists in the given path, an exception is thrown, which is the default mode for writing data
SaveMode.Append	Data is written appending
SaveMode.Overwrite	Data is written as an overlay
SaveMode.Ignore	If the file already exists in the given path, nothing is done

Second, the CSV

CSV is a common text file format in which each line represents a record and each field in the record is separated by a comma.

2.1 Reading a CSV File

Automatic inference type read Read examples:

spark.read.format("csv")
.option("header"."false")        // Whether the first line in the file is the column name
.option("mode"."FAILFAST")      // Whether to fail quickly
.option("inferSchema"."true")   // Whether to automatically infer schema
.load("/usr/file/csv/dept.csv")
.show()
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Using predefined types:

import org.apache.spark.sql.types.{StructField, StructType, StringType,LongType}
// Predefined data format
val myManualSchema = new StructType(Array(
    StructField("deptno", LongType, nullable = false),
    StructField("dname", StringType,nullable = true),
    StructField("loc", StringType,nullable = true)
))
spark.read.format("csv")
.option("mode"."FAILFAST")
.schema(myManualSchema)
.load("/usr/file/csv/dept.csv")
.show()
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2.2 Writing a CSV file

df.write.format("csv").mode("overwrite").save("/tmp/csv/dept2")
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You can also specify a specific delimiter:

df.write.format("csv").mode("overwrite").option("sep"."\t").save("/tmp/csv/dept2")
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2.3 Optional Configuration

To save space, see section 9.1 at the end of this article for all read and write configuration items.

Third, the JSON

3.1 Reading a JSON File

spark.read.format("json").option("mode", "FAILFAST").load("/usr/file/json/dept.json").show(5)
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Note that a single data record spanning multiple lines is not supported by default (as shown below). This can be changed by setting multiLine to true, which defaults to false.

// Default support for single line {"DEPTNO": 10,"DNAME": "ACCOUNTING","LOC": "NEW YORK"} "ACCOUNTING", "LOC": "NEW YORK" }Copy the code

3.2 Writing a JSON File

df.write.format("json").mode("overwrite").save("/tmp/spark/json/dept")
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3.3 Optional Configuration

To save space, see section 9.2 at the end of this article for all read and write configuration items.

Four, Parquet

Parquet is an open source, column-oriented data store that provides a variety of storage optimizations to allow reading of individual columns rather than entire files, saving storage space and improving read efficiency. It is the default file format of Spark.

4.1 Reading the Parquet file

spark.read.format("parquet").load("/usr/file/parquet/dept.parquet").show(5)
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2.2 Writing to the Parquet file

df.write.format("parquet").mode("overwrite").save("/tmp/spark/parquet/dept")
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2.3 Optional Configuration

The Parquet file has its own storage rules, so it has few optional configuration items. The commonly used ones are as follows:

Read and write operations	Configuration items	An optional value	The default value	describe
Write	compression or codec	None, uncompressed, bzip2, deflate, gzip, lz4, or snappy	None	Compressed file format
Read	mergeSchema	true, false	Depending on the configuration itemspark.sql.parquet.mergeSchema	When true, the Parquet data source consolidates all the schemas collected by the data files, otherwise the Schema is selected from the summary file, or from the random data file if no summary file is available.

More optional configuration can refer to the official document: spark.apache.org/docs/latest…

Five, the ORC

ORC is a self-describing, type-aware column file format that is optimized for reading and writing large data and is a common file format for big data.

5.1 Reading ORC Files

spark.read.format("orc").load("/usr/file/orc/dept.orc").show(5)
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4.2 Writing ORC Files

csvFile.write.format("orc").mode("overwrite").save("/tmp/spark/orc/dept")
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Six, SQL Databases

Spark also supports data reads and writes with traditional relational databases. However, Spark does not provide the database driver by default. To use the Spark database driver, you need to upload the database driver to the jars directory in the installation directory. The following example uses the Mysql database. Before using the Mysql database, you need to upload the mysql-connector-java-x.x.x.jar to the jars directory.

6.1 Reading Data

Help_keyword = help_keyword_id; help_keyword_id = name;

spark.read
.format("jdbc")
.option("driver"."com.mysql.jdbc.Driver")            / / driver
.option("url"."JDBC: mysql: / / 127.0.0.1:3306 / mysql")   // Database address
.option("dbtable"."help_keyword")                    / / the name of the table
.option("user"."root").option("password"."root").load().show(10)
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Read data from query results:

val pushDownQuery = """(SELECT * FROM help_keyword WHERE help_keyword_id <20) AS help_keywords"""
spark.read.format("jdbc")
.option("url"."JDBC: mysql: / / 127.0.0.1:3306 / mysql")
.option("driver"."com.mysql.jdbc.Driver")
.option("user"."root").option("password"."root")
.option("dbtable", pushDownQuery)
.load().show()

/ / output
+---------------+-----------+
|help_keyword_id|       name|
+---------------+-----------+
|              0|         <>|
|              1|     ACTION|
|              2|        ADD|
|              3|AES_DECRYPT|
|              4|AES_ENCRYPT|
|              5|      AFTER|
|              6|    AGAINST|
|              7|  AGGREGATE|
|              8|  ALGORITHM|
|              9|        ALL|
|             10|      ALTER|
|             11|    ANALYSE|
|             12|    ANALYZE|
|             13|        AND|
|             14|    ARCHIVE|
|             15|       AREA|
|             16|         AS|
|             17|   ASBINARY|
|             18|        ASC|
|             19|     ASTEXT|
+---------------+-----------+
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You can also use the following notation for data filtering:

val props = new java.util.Properties
props.setProperty("driver"."com.mysql.jdbc.Driver")
props.setProperty("user"."root")
props.setProperty("password"."root")
val predicates = Array("help_keyword_id < 10 OR name = 'WHEN'")   // Specify data filtering criteria
spark.read.jdbc("JDBC: mysql: / / 127.0.0.1:3306 / mysql"."help_keyword", predicates, props).show() 

/ / output:
+---------------+-----------+
|help_keyword_id|       name|
+---------------+-----------+
|              0|         <>|
|              1|     ACTION|
|              2|        ADD|
|              3|AES_DECRYPT|
|              4|AES_ENCRYPT|
|              5|      AFTER|
|              6|    AGAINST|
|              7|  AGGREGATE|
|              8|  ALGORITHM|
|              9|        ALL|
|            604|       WHEN|
+---------------+-----------+
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NumPartitions can be used to specify the degree of parallelism for reading data:

option("numPartitions".10)
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Here, in addition to specifying partitions, you can also set upper and lower bounds, with any value less than the lower bound assigned to the first partition and any value greater than the upper bound assigned to the last partition.

val colName = "help_keyword_id"   // The column used to judge the upper and lower bounds
val lowerBound = 300L    / / lower
val upperBound = 500L    / / the upper bound
val numPartitions = 10   // Partition overview
val jdbcDf = spark.read.jdbc("JDBC: mysql: / / 127.0.0.1:3306 / mysql"."help_keyword",
                             colName,lowerBound,upperBound,numPartitions,props)
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To verify the contents of a partition, use the operator mapPartitionsWithIndex as follows:

jdbcDf.rdd.mapPartitionsWithIndex((index, iterator) => {
    val buffer = new ListBuffer[String]
    while (iterator.hasNext) {
        buffer.append(index + "Partition." + iterator.next())
    }
    buffer.toIterator
}).foreach(println)
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The result is as follows: The help_keyword table contains only about 600 entries, which should be distributed evenly across 10 partitions. Instead, partition 0 contains 319 entries, because the lower limit is set so that all entries less than 300 are restricted to the first partition, partition 0. Similarly, all data greater than 500 is allocated to partition 9, the last partition.

6.2 Writing Data

val df = spark.read.format("json").load("/usr/file/json/emp.json")
df.write
.format("jdbc")
.option("url"."JDBC: mysql: / / 127.0.0.1:3306 / mysql")
.option("user"."root").option("password"."root")
.option("dbtable"."emp")
.save()
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Seven, the Text

Text files have no advantages in read/write performance and cannot express clear data structures, so they are rarely used. Read/write operations are as follows:

7.1 Reading Text Data

spark.read.textFile("/usr/file/txt/dept.txt").show()
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7.2 Writing Text Data

df.write.text("/tmp/spark/txt/dept")
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Advanced data read and write features

8.1 read in parallel

Multiple Executors can’t read the same file at the same time, but they can read different files at the same time. This means that when you read data from a folder containing multiple files, each of those files will become a partition in the DataFrame and be read by available Executors in parallel.

8.2 parallel write

The number of files or data written depends on the number of partitions owned by the DataFrame at the time the data is written. By default, one file is written per data partition.

8.3 Partition Writing

The concepts of partition and bucket are the same as those of partition tables and bucket tables in Hive. All data are split and stored according to certain rules. Note that partitions specified by partitionBy are not the same concept as partitions in RDD: partitions are represented as subdirectories of the output directory, and data is stored in the corresponding subdirectories.

val df = spark.read.format("json").load("/usr/file/json/emp.json")
df.write.mode("overwrite").partitionBy("deptno").save("/tmp/spark/partitions")
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The output is as follows: You can see that the output is divided into three subdirectories according to the department number. The output files are in the subdirectories.

8.3 Write data by bucket

Bucket write hashes data based on the specified number of columns and buckets. Currently, bucket write can only be saved as a table, which is actually a bucket table of Hive.

val numberBuckets = 10
val columnToBucketBy = "empno"
df.write.format("parquet").mode("overwrite")
.bucketBy(numberBuckets, columnToBucketBy).saveAsTable("bucketedFiles")
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8.5 File Size Management

If the number of small files generated by the write is too high, then there is a lot of metadata overhead. Spark and HDFS cannot handle this problem, which is called a small file problem. At the same time, the data file should not be too large, otherwise there will be unnecessary performance overhead in the query, so the file size should be controlled within a reasonable range.

We have already shown that you can indirectly control file size by controlling the number of generated files through the number of partitions. Spark 2.2 introduces a new way to control file size in a more automated way, the maxRecordsPerFile parameter, which allows you to control file size by controlling the number of records written to the file.

 // Spark will ensure that the file contains a maximum of 5000 recordsDf. Write. Option (" maxRecordsPerFile ",5000)
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9. Optional Configuration Appendix

9.1 READING and Writing CSV Optional configuration

Read/write operation	Configuration items	An optional value	The default value	describe
Both	seq	Any character	.(comma)	The separator
Both	header	true, false	false	Whether the first line in the file is the name of the column.
Read	escape	Any character	\	Escape character
Read	inferSchema	true, false	false	Whether the column type is automatically inferred
Read	ignoreLeadingWhiteSpace	true, false	false	Whether to skip the space before the value
Both	ignoreTrailingWhiteSpace	true, false	false	Whether to skip the space after the value
Both	nullValue	Any character	“”	Declare which character in the file represents a null value
Both	nanValue	Any character	NaN	Which value is declared to represent NaN or the default value
Both	positiveInf	Any character	Inf	Is infinite
Both	negativeInf	Any character	-Inf	Minus infinity
Both	compression or codec	None, uncompressed, bzip2, deflate, gzip, lz4, or snappy	none	File compression format
Both	dateFormat	Anything that can be converted to Java String of SimpleDataFormat	yyyy-MM-dd	The date format
Both	timestampFormat	Anything that can be converted to Java String of SimpleDataFormat	Yyyy – MMdd ‘T’ HH: mm: ss. SSSZZ	Timestamp format
Read	maxColumns	Arbitrary integer	20480	Declares the maximum number of columns in a file
Read	maxCharsPerColumn	Arbitrary integer	1000000	Declares the maximum number of characters in a column.
Read	escapeQuotes	true, false	true	Whether quotes in lines should be escaped.
Read	maxMalformedLogPerPartition	Arbitrary integer	10	Specifies the maximum number of malformed data allowed in each partition, beyond which malformed data will not be read
Write	quoteAll	true, false	false	Specifies whether all values should be enclosed in quotes, rather than just escaping values with quoted characters.
Read	multiLine	true, false	false	Whether to allow multiple rows across domains for each complete record

9.2 JSON Read/Write Optional configuration

Read/write operation	Configuration items	An optional value	The default value
Both	compression or codec	None, uncompressed, bzip2, deflate, gzip, lz4, or snappy	none
Both	dateFormat	Any string that can be converted to Java’s SimpleDataFormat	yyyy-MM-dd
Both	timestampFormat	Any string that can be converted to Java’s SimpleDataFormat	Yyyy – MMdd ‘T’ HH: mm: ss. SSSZZ
Read	primitiveAsString	true, false	false
Read	allowComments	true, false	false
Read	allowUnquotedFieldNames	true, false	false
Read	allowSingleQuotes	true, false	true
Read	allowNumericLeadingZeros	true, false	false
Read	allowBackslashEscapingAnyCharacter	true, false	false
Read	columnNameOfCorruptRecord	true, false	Value of spark.sql.column&NameOf
Read	multiLine	true, false	false

9.3 Database Read/Write Optional configuration

The attribute name	meaning
url	Database address
dbtable	The name of the table
driver	Database driver
partitionColumn, lowerBound, upperBoun	Total number of partitions, upper bound, lower bound
numPartitions	Maximum number of partitions that can be used for table read and write parallelism. If the number of partitions to be written exceeds this limit, you can call coalesce(numPartition) to reset the number of partitions.
fetchsize	How many rows of data are fetched per round trip. This option only applies to reading data.
batchsize	How many rows are inserted per round trip? This option only applies to writes. The default value is 1000.
isolationLevel	Transaction isolation level: Can be NONE, READ_COMMITTED, READ_UNCOMMITTED, REPEATABLE_READ or SERIALIZABLE, which is the standard transaction isolation level. The default value is READ_UNCOMMITTED. This option only applies to data reads.
createTableOptions	Customize configurations for creating tables while writing data
createTableColumnTypes	Customize column types to create columns when writing data

Database to read and write more configuration can refer to the official document: spark.apache.org/docs/latest…

The resources

1. Matei Zaharia, Bill Chambers . Spark: The Definitive Guide[M] . 2018-02
2. Spark.apache.org/docs/latest…

See the GitHub Open Source Project: Getting Started with Big Data for more articles in the big Data series