Spark笔记 11_10_2017

1 spark 读入数据
spark.read.csv(,,,,)

• path: location of files. Similar to Spark can accept standard Hadoop globbing expressions.
• header: when set to true ,the first line of files will be used to name columns and will not be included in data. All types will be assumed string. Default value is false.
• delimiter: by default columns are delimited using ,, but delimiter can be set to any character
• quote: by default the quote character is “, but can be set to any character. Delimiters inside quotes are ignored
• escape: by default the escape character is , but can be set to any character. Escaped quote characters are ignored
• parserLib: by default it is “commons” can be set to “univocity” to use that library for CSV parsing.
• mode: determines the parsing mode. By default it is PERMISSIVE. Possible values are:
• PERMISSIVE: tries to parse all lines: nulls are inserted for missing tokens and extra tokens are ignored.
• DROPMALFORMED: drops lines which have fewer or more tokens than expected or tokens which do not match the schema
• FAILFAST: aborts with a RuntimeException if encounters any malformed line
• charset: defaults to ‘UTF-8’ but can be set to other valid charset names
• inferSchema: automatically infers column types. It requires one extra pass over the data and is false by default
• comment: skip lines beginning with this character. Default is “#”. Disable comments by setting this to null.
• nullValue: specifies a string that indicates a null value, any fields matching this string will be set as nulls in the DataFrame
• dateFormat: specifies a string that indicates the date format to use when reading dates or timestamps. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to both DateType and TimestampType. By default, it is null which means trying to parse times and date by java.sql.Timestamp.valueOf() and java.sql.Date.valueOf().

Spark 1.4+:

Automatically infer schema (data types), otherwise everything is assumed string:

from pyspark.sql import SQLContextsqlContext = SQLContext(sc)df = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('cars.csv')df.select('year', 'model').write.format('com.databricks.spark.csv').save('newcars.csv')

You can manually specify schema:

from pyspark.sql import SQLContextfrom pyspark.sql.types import *sqlContext = SQLContext(sc)customSchema = StructType([ \    StructField("year", IntegerType(), True), \    StructField("make", StringType(), True), \    StructField("model", StringType(), True), \    StructField("comment", StringType(), True), \    StructField("blank", StringType(), True)])df = sqlContext.read \    .format('com.databricks.spark.csv') \    .options(header='true') \    .load('cars.csv', schema = customSchema)df.select('year', 'model').write \    .format('com.databricks.spark.csv') \    .save('newcars.csv')

You can save with compressed output:

from pyspark.sql import SQLContextsqlContext = SQLContext(sc)df = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('cars.csv')df.select('year', 'model').write.format('com.databricks.spark.csv').options(codec="org.apache.hadoop.io.compress.GzipCodec").save('newcars.csv')

Programmatically Specifying the Schema

When a dictionary of kwargs cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), a DataFrame can be created programmatically with three steps.

Create an RDD of tuples or lists from the original RDD;
Create the schema represented by a StructType matching the structure of tuples or lists in the RDD created in the step 1.
Apply the schema to the RDD via createDataFrame method provided by SparkSession.
For example:

# Import data typesfrom pyspark.sql.types import *sc = spark.sparkContext# Load a text file and convert each line to a Row.lines = sc.textFile("examples/src/main/resources/people.txt")parts = lines.map(lambda l: l.split(","))# Each line is converted to a tuple.people = parts.map(lambda p: (p[0], p[1].strip()))# The schema is encoded in a string.schemaString = "name age"fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]schema = StructType(fields)# Apply the schema to the RDD.schemaPeople = spark.createDataFrame(people, schema)# Creates a temporary view using the DataFrameschemaPeople.createOrReplaceTempView("people")# SQL can be run over DataFrames that have been registered as a table.results = spark.sql("SELECT name FROM people")results.show()# +-------+# |   name|# +-------+# |Michael|# |   Andy|# | Justin|# +-------+

参考：https://github.com/databricks/spark-csv