Apache Spark - Loading & Saving data | Big Data Hadoop Spark Tutorial | CloudxLab

Loading + Saving Data
1. So far we
a. either converted in-memory data
b. Or used the HDFS file
Loading & Saving Data

1. So far we
a. either converted in-memory data
b. Or used the HDFS file
2. Spark supports wide variety of datasets
3. Can access data through InputFormat & OutputFormat
a. The interfaces used by Hadoop
b. Available for many common file formats and storage
systems (e.g., S3, HDFS, Cassandra, HBase, etc.).

Common Data Sources
File formats Stores

Common Data Sources
File formats Stores
● Text, JSON, SequenceFiles,
Protocol buffers.
● We can also configure
compression

Common Data Sources
File formats Stores
● Text, JSON, SequenceFiles,
Protocol buffers.
● We can also configure
compression
Filesystems
● Local, NFS, HDFS, Amazon S3
Databases and key/value stores
● For Cassandra, HBase, Elasticsearch,
and JDBC databases.

Structured data sources through Spark SQL aka Data Frames
+ Efficient API for structured data sources, including JSON and Apache Hive
+ Covered later

Common supported file formats
+ A file could be in any format
+ If we know upfront, we can read it and load it
+ Else we can use “file” command like tool

+ Very common. Plain old text files. Printable chars
+ Records are assumed to be one per line.
+ Unstructured Data
Text files
Example:

+ Javascript Object Notation
+ Common text-based format
+ Semistructured; most libraries require one record per line.
JSON files
{
"name" : "John",
"age" : 31,
"knows" : [“C”, “C++”]
}
Example:

+ Very common text-based format
+ Often used with spreadsheet applications.
+ Comma separated Values
CSV files
Example:

+ Compact Hadoop file format used for key/value data.
+ Key and values can be binary data
+ To bundle together many small files
Sequence files
See More at https://wiki.apache.org/hadoop/SequenceFile

+ A fast, space-efficient multilanguage format.
+ More compact than JSON.
Protocol buffers
See More at https://developers.google.com/protocol-buffers/
message Person {
required string name = 1;
required int32 id = 2;
optional string email = 3;
}

+ For data from a Spark job to be consumed by another
+ Breaks if you change your classes - Java Serialization.
Object Files

Loading Files
var input = sc.textFile("/data/ml-100k/u1.test")
Handling Text Files - scala

Loading Files
Loading Directories
var input = sc.wholeTextFiles("/data/ml-100k");
var lengths = input.mapValues(x => x.length);
lengths.collect();
[(u'hdfs://ip-172-31-53-48.ec2.internal:8020/data/ml-100k/mku.sh', 643),
(u'hdfs://ip-172-31-53-48.ec2.internal:8020/data/ml-100k/u.data', 1979173),
(u'hdfs://ip-172-31-53-48.ec2.internal:8020/data/ml-100k/u.genre', 202),
(u'hdfs://ip-172-31-53-48.ec2.internal:8020/data/ml-100k/u.info', 36) …]

Loading Files
Loading Directories
var input = sc.wholeTextFiles("/data/ml-100k");
var lengths = input.mapValues(x => x.length);
lengths.collect();
[(u'hdfs://ip-172-31-53-48.ec2.internal:8020/data/ml-100k/mku.sh', 643),
(u'hdfs://ip-172-31-53-48.ec2.internal:8020/data/ml-100k/u.data', 1979173),
(u'hdfs://ip-172-31-53-48.ec2.internal:8020/data/ml-100k/u.genre', 202),
(u'hdfs://ip-172-31-53-48.ec2.internal:8020/data/ml-100k/u.info', 36) …]
Saving Files
lengths.saveAsTextFile(outputDir)

1. Records are stored one per line,
2. Fixed number of fields per line
3. Fields are separated by a comma (tab in TSV)
4. We get row number to detect header etc.
Comma / Tab -Separated Values (CSV / TSV)

Data: /data/spark/temps.csv
Loading CSV - Sample Data
20, NYC, 2014-01-01
20, NYC, 2015-01-01
21, NYC, 2014-01-02
23, BLR, 2012-01-01
25, SEATLE, 2016-01-01
21, CHICAGO, 2013-01-05
24, NYC, 2016-5-05

Loading CSV - Simple Approach
Array(
Array(20, " NYC", " 2014-01-01"),
Array(20, " NYC", " 2015-01-01"),
Array(21, " NYC", " 2014-01-02"),
Array(23, " BLR", " 2012-01-01"),
Array(25, " SEATLE", " 2016-01-01"),
Array(21, " CHICAGO", " 2013-01-05"),
Array(24, " NYC", " 2016-5-05")
)
var lines = sc.textFile("/data/spark/temps.csv");
var recordsRDD = lines.map(line => line.split(","));
recordsRDD.take(10);

import au.com.bytecode.opencsv.CSVParser
var a = sc.textFile("/data/spark/temps.csv");
var p = a.map(
line => {
val parser = new CSVParser(',')
parser.parseLine(line)
})
p.take(1)
//Array(Array(20, " NYC", " 2014-01-01"))
spark-shell --packages net.sf.opencsv:opencsv:2.3
Or
Add this to sbt: libraryDependencies += "net.sf.opencsv" % "opencsv" % "2.3"
Loading CSV - Example
https://gist.github.com/girisandeep/b721cf93981c338665c328441d419253

Loading CSV - Example Efficient
https://gist.github.com/girisandeep/fddf49ef97fde429a0d3256160b257c1

var linesRdd = sc.textFile("/data/spark/temps.csv");

def parseCSV(itr:Iterator[String]):Iterator[Array[String]] = {
for(line <- itr)
yield parser.parseLine(line)
}

for(line <- itr)
}
//Check with simple example
val x = parseCSV(Array("1,2,3","a,b,c").iterator)
val result = linesRdd.mapPartitions(parseCSV)

for(line <- itr)
}
//Check with simple example
val x = parseCSV(Array("1,2,3","a,b,c").iterator)
val result = linesRdd.mapPartitions(parseCSV)
result.take(1)
//Array[Array[String]] = Array(Array(20, " NYC", " 2014-01-01"))

Tab Separated Files
Similar to csv:
val parser = new CSVParser('t')

● Popular Hadoop format
○ For handling small files
○ Create InputSplits without too much transport
SequenceFiles

SequenceFiles
● Popular Hadoop format
○ For handling small files
○ Create InputSplits without too much transport
● Composed of flat files with key/value pairs.
● Has Sync markers
○ Allow to seek to a point
○ Then resynchronize with the record boundaries
○ Allows Spark to efficiently read in parallel from multiple nodes

Loading SequenceFiles
val data = sc.sequenceFile(inFile,
"org.apache.hadoop.io.Text", "org.apache.hadoop.io.IntWritable")
data.map(func)
…
data.saveAsSequenceFile(outputFile)

var rdd = sc.parallelize(Array(("key1", 1.0), ("key2", 2.0), ("key3", 3.0)))
rdd.saveAsSequenceFile("pysequencefile1")
Saving SequenceFiles - Example

import org.apache.hadoop.io.DoubleWritable
import org.apache.hadoop.io.Text
Loading SequenceFiles - Example

val myrdd = sc.sequenceFile(
"pysequencefile1",
classOf[Text], classOf[DoubleWritable])

val myrdd = sc.sequenceFile(
"pysequencefile1",
classOf[Text], classOf[DoubleWritable])
val result = myrdd.map{case (x, y) => (x.toString, y.get())}
result.collect()
//Array((key1,1.0), (key2,2.0), (key3,3.0))

● Simple wrapper around SequenceFiles
● Values are written out using Java Serialization.
● Intended to be used for Spark jobs communicating with other
Spark jobs
● Can also be quite slow.
Object Files

Object Files
● Saving - saveAsObjectFile() on an RDD
● Loading - objectFile() on SparkContext
● Require almost no work to save almost arbitrary objects.
● Not available in python using pickle file instead
● If you change the objects, old files may not be valid

Pickle File
● Python way of handling object files
● Uses Python’s pickle serialization library
● Saving - saveAsPickleFile() on an RDD
● Loading - pickleFile() on SparkContext
● Can also be quite slow as Object Fiels

● Access Hadoop-supported storage formats
● Many key/value stores provide Hadoop input formats
● Example providers:HBase, MongoDB
● Older: hadoopFile() / saveAsHadoopFile()
● Newer: newAPIHadoopDataset() / saveAsNewAPIHadoopDataset()
● Takes a Configuration object on which you set the Hadoop properties
Non-filesystem data sources - hadoopFile

Read an ‘old’ Hadoop InputFormat with arbitrary key and value class from HDFS, a local file
system (available on all nodes), or any Hadoop-supported file system URI. The mechanism is
the same as for sc.sequenceFile.
A Hadoop configuration can be passed in as a Python dict. This will be converted into a
Configuration in Java.
Parameters:
path – path to Hadoop file
inputFormatClass – fully qualified classname of Hadoop InputFormat (e.g. “org.apache.hadoop.mapred.TextInputFormat”)
keyClass – fully qualified classname of key Writable class (e.g. “org.apache.hadoop.io.Text”)
valueClass – fully qualified classname of value Writable class (e.g. “org.apache.hadoop.io.LongWritable”)
keyConverter – (None by default)
valueConverter – (None by default)
conf – Hadoop configuration, passed in as a dict (None by default)
batchSize – The number of Python objects represented as a single Java object. (default 0, choose batchSize automatically)
Hadoop Input and Output Formats - Old API
hadoopFile(path, inputFormatClass, keyClass, valueClass, keyConverter=None,
valueConverter=None, conf=None, batchSize=0)

Read a ‘new API’ Hadoop InputFormat with arbitrary key and value class from HDFS, a local
file system (available on all nodes), or any Hadoop-supported file system URI. The mechanism
is the same as for sc.sequenceFile.
A Hadoop configuration can be passed in as a Python dict. This will be converted into a
Configuration in Java
Parameters:
path – path to Hadoop file
inputFormatClass – fully qualified classname of Hadoop InputFormat (e.g.
“org.apache.hadoop.mapreduce.lib.input.TextInputFormat”)
keyClass – fully qualified classname of key Writable class (e.g. “org.apache.hadoop.io.Text”)
valueClass – fully qualified classname of value Writable class (e.g. “org.apache.hadoop.io.LongWritable”)
keyConverter – (None by default)
valueConverter – (None by default)
conf – Hadoop configuration, passed in as a dict (None by default)
batchSize – The number of Python objects represented as a single Java object. (default 0, choose batchSize automatically)
Hadoop Input and Output Formats - New API
newAPIHadoopFile(path, inputFormatClass, keyClass, valueClass, keyConverter=None,
valueConverter=None, conf=None, batchSize=0)

See https://databricks.com/blog/2015/03/20/using-mongodb-with-spark.html
# set up parameters for reading from MongoDB via Hadoop input format
config = {"mongo.input.uri": "mongodb://localhost:27017/marketdata.minbars"}
inputFormatClassName = "com.mongodb.hadoop.MongoInputFormat"
keyClassName = "org.apache.hadoop.io.Text"
valueClassName = "org.apache.hadoop.io.MapWritable"
# read the 1-minute bars from MongoDB into Spark RDD format
minBarRawRDD = sc.newAPIHadoopRDD(inputFormatClassName, keyClassName,
valueClassName, None, None, config)
Hadoop Input and Output Formats - Old API
Loading Data from mongodb

● Developed at Google for internal RPCs
● Open sourced
● Structured data - fields & types of fields defined
● Fast for encoding and decoding (20-100x than XML)
● Take up the minimum space (3-10x than xml)
● Defined using a domain-specific language
● Compiler generates accessor methods in variety of languages
● Consist of fields: optional, required, or repeated
● While parsing
○ A missing optional field => success
○ A missing required field => failure
● So, make new fields as optional (remember object file failures?)
Protocol buffers

Protocol buffers - Example
package tutorial;
message Person {
required string name = 1;
required int32 id = 2;
optional string email = 3;
enum PhoneType {
MOBILE = 0;
HOME = 1;
WORK = 2;
}
message PhoneNumber {
required string number = 1;
optional PhoneType type = 2 [default = HOME];
}
repeated PhoneNumber phone = 4;
}
message AddressBook {
repeated Person person = 1;
}

1. Download and install protocol buffer compiler
2. pip install protobuf
3. protoc -I=$SRC_DIR --python_out=$DST_DIR
$SRC_DIR/addressbook.proto
4. create objects
5. Convert those into protocol buffers
6. See this project
Protocol buffers - Steps

1. To Save Storage & Network Overhead
2. With most hadoop output formats we can specify compression codecs
3. Compression should not require the whole file at once
4. Each worker can find start of record => splitable
5. You can configure HDP for LZO using Ambari:
http://docs.hortonworks.com/HDPDocuments/Ambari-2.2.2.0/bk_ambari
_reference_guide/content/_configure_core-sitexml_for_lzo.html
File Compression

File Compression Options
Characteristics of compression:
● Splittable
● Speed
● Effectiveness on Text
● Code

Format Splittable Spee
d
Effectiveness on
text
Hadoop compression codec comments
gzip N Fast High org.apache.hadoop.io.com.GzipCodec
lzo Y V.
Fast
Medium com.hadoop.compression.lzo.LzoCode
c
LZO requires
installation on
every worker
node
bzip2 Y Slow V. High org.apache.hadoop.io.com.BZip2Codec Uses pure Java
for splittable
version
zlib N Slow Medium org.apache.hadoop.io.com.DefaultCode
c
Default
compression
codec for
Hadoop
Snappy N V.
Fast
Low org.apache.hadoop.io.com.SnappyCod
ec
There is a pure
Java port of
Snappy but it is
not yet available
in Spark/
Hadoop
File Compression Options

1. Enable in HADOOP by updating the conf of hadoop
http://docs.hortonworks.com/HDPDocuments/Ambari-2.2.2.0/bk_ambari_r
eference_guide/content/_configure_core-sitexml_for_lzo.html
2. Create data:
$ bzip2 --stdout file.bz2 | lzop -o file.lzo
3. Update Spark-env.sh with
export SPARK_CLASSPATH=$SPARK_CLASSPATH:hadoop-lzo-0.4.20-SNAPSHOT.jar
In your code, use:
conf.set("io.compression.codecs”, "com.hadoop.compression.lzo.LzopCodec”);
Ref: https://gist.github.com/zedar/c43cbc7ff7f98abee885
Handling LZO

Loading + Saving Data: File Systems

1. rdd = sc.textFile("file:///home/holden/happypandas.gz")
2. The path has to be available on all nodes.
Otherwise, load it locally and distribute using sc.parallelize
Local/“Regular” FS

1. Popular option
2. Good if nodes are inside EC2
3. Use path in all input methods (textFile, hadoopFile etc)
s3n://bucket/path-within-bucket
4. Set Env. Vars: AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY
More details: https://cloudxlab.com/blog/access-s3-files-spark/
Amazon S3

1. The Hadoop Distributed File System
2. Spark and HDFS can be collocated on the same machines
3. Spark can take advantage of this data locality to avoid network overhead
4. In all i/o methods, use path: hdfs://master:port/path
5. Use only the version of spark w.r.t HDFS version
HDFS

Thank you!
Spark - Loading + Saving Data

Apache Spark - Loading & Saving data | Big Data Hadoop Spark Tutorial | CloudxLab

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