Hadoop_Ch.4.Hadoop.I.O

Hadoop: The Definitive Guide
Chap. 4 Hadoop I/O
Kisung Kim
Contents
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Integrity
Compression
Serialization
File-based Data Structure
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Data Integrity
 When the volumes of data flowing through the system are as large as
the ones Hadoop is capable of handling, the chance of data corruption
occurring is high
 Checksum
– Usual way of detecting corrupted data
– Technique for only error detection (cannot fix the corrupted data)
– CRC-32 (cyclic redundancy check)
 Compute a 32-bit integer checksum for input of any size
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Data Integrity in HDFS
 HDFS transparently checksums all data written to it and by default verifies
checksums when reading data
– io.bytes.per.checksum
 Data size to compute checksums
 Default is 512 bytes
 Datanodes are responsible for verifying the data they receive before storing
the data and its checksum
– If it detects an error, the client receives a ChecksumException, a subclass of
IOException
 When clients read data from datanodes, they verify checksums as well,
comparing them with the ones stored at the datanode
 Checksum verification log
– Each datanode keeps a persistent log to know the last time each of its blocks was
verified
– When a client successfully verifies a block, it tells the datanode who sends the
block
– Then, the datanode updates its log
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Data Integrity in HDFS
 DataBlockScanner
– Background thread that periodically verifies all the blocks stored on the
datanode
– Guard against corruption due to “bit rot” in the physical storage media
 Healing corrupted blocks
– If a client detects an error when reading a block, it reports the bad block and
the datanode to the namenode
– Namenode marks the block replica as corrupt
– Namenode schedules a copy of the block to be replicated on another
datanode
– The corrupt replica is deleted
 Disabling verification of checksum
– Pass false to the setVerifyCheckSum() method on FileSystem
– -ignoreCrc option
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Data Integrity in HDFS
 LocalFileSystem
– Performes client-side checksumming
– When you write a file called filename, the FS client transparently creates a
hidden file, .filename.crc, in the same directory containing the checksums for
each chunk of the file
 RawLocalFileSystem
– Disable checksums
– Use when you don’t need checksums
 ChecksumFileSystem
– Wrapper around FileSystem
– Make it easy to add checksumming to other (nonchecksummed) FS
– Underlying FS is called the raw FS
FileSystem rawFs = ...
FileSystem checksummedFs = new ChecksumFileSystem(rawFs);
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Compression
 Two major benefits of file compression
– Reduce the space needed to store files
– Speed up data transfer across the network
 When dealing with large volumes of data, both of these savings can be
significant, so it pays to carefully consider how to use compression in
Hadoop
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Compression Formats
 Compression formats
Compression
Format
Tool
Algorithm
Filename
Extension
Multiple Files
Splittable
DEFLATE
N/A
DEFLATE
.deflate
NO
NO
gzip
gzip
DEFLATE
.gz
NO
NO
ZIP
zip
DEFLATE
.zip
YES
YES, at file
boundaries
bzip2
bzip2
bzip2
.bz2
NO
YES
LZO
lzop
LZO
.lzo
NO
NO
 “Splittable” column
– Indicates whether the compression format supports splitting
– Whether you can seek to any point in the stream and start reading from
some point further on
– Splittable compression formats are especially suitable for MapReduce
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Codes
 Implementation of a compression-decompression algorithm
Compression Format
Hadoop Compression Codec
DEFLATE
org.apache.hadoop.io.compression.DefaultCodec
gzip
org.apache.hadoop.io.compression.GzipCodec
Bzip2
org.apache.hadoop.io.compression.BZip2Codec
LZO
com.hadoop.compression.lzo.LzopCodec
 The LZO libraries are GPL-licensed and may not be included in Apache
distributions
 CompressionCodec
– createOutputStream(OutputStream out): create a
CompressionOutputStream to which you write your uncompressed data to
have it written in compressed form to the underlying stream
– createInputStream(InputStream in): obtain a CompressionInputStream,
which allows you to read uncompressed data from the underlying stream
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Example
public class StreamCompressor {
public static void main(String[] args) throws Exception {
String codecClassname = args[0];
Class<?> codecClass = Class.forName(codecClassname);
Configuration conf = new Configuration();
CompressionCodec codec = (CompressionCodec)
ReflectionUtils.newInstance(codecClass, conf);
CompressionOutputStream out = codec.createOutputStream(System.out);
IOUtils.copyBytes(System.in, out, 4096, false);
out.finish();
}
}
 finish()
– Tell the compressor to finish writing to the compressed stream, but doesn’t
close the stream
% echo "Text" | hadoop StreamCompressor org.apache.hadoop.io.compress.GzipCodec \
| gunzip Text
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Compression and Input Splits
 When considering how to compress data that will be processed by
MapReduce, it is important to understand whether the compression
format supports splitting
 Example of not-splitable compression problem
– A file is a gzip-compressed file whose compressed size is 1 GB
– Creating a split for each block won’t work since it is impossible to start
reading at an arbitrary point in the gzip stream, and therefore impossible for
a map task to read its split independently of the others
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Serialization
 Process of turning structured objects into a byte stream for
transmission over a network or for writing to persistent storage
 Deserialization is the reverse process of serialization
 Requirements
– Compact
 To make efficient use of storage space
– Fast
 The overhead in reading and writing of data is minimal
– Extensible
 We can transparently read data written in an older format
– Interoperable
 We can read or write persistent data using different language
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Writable Interface
 Writable interface defines two methods
– write() for writing its state to a DataOutput binary stream
– readFields() for reading its state from a DataInput binary stream
public interface Writable {
void write(DataOutput out) throws IOException;
void readFields(DataInput in) throws IOException;
}
 Example: IntWritable
IntWritable writable = new IntWritable();
writable.set(163);
public static byte[] serialize(Writable writable) throws IOException {
ByteArrayOutputStream out = new ByteArrayOutputStream();
DataOutputStream dataOut = new DataOutputStream(out);
writable.write(dataOut);
dataOut.close();
return out.toByteArray();
}
byte[] bytes = serialize(writable);
assertThat(bytes.length, is(4));
assertThat(StringUtils.byteToHexString(bytes), is("000000a3"));
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WritableComparable and Comparator
 IntWritable implements the WritableComparable interface
public interface WritableComparable<T> extends Writable, Comparable<T> {
}
 Comparison of types is crucial for MapReduce
 Optimization: RawComparator
– Compare records read from a stream without deserializing them into objects
 WritableComparator is a general-purpose implementation of
RawComparator
– Provide a default implementation of the raw compare() method
 Deserialize the objects and invokes the object compare() method
– Act as a factory for RawComparator instances
RawComparator<IntWritable> comparator = WritableComparator.get(IntWritable.class);
IntWritable w1 = new IntWritable(163);
IntWritable w2 = new IntWritable(67);
assertThat(comparator.compare(w1, w2), greaterThan(0));
byte[] b1 = serialize(w1); byte[] b2 = serialize(w2);
assertThat(comparator.compare(b1, 0, b1.length, b2, 0, b2.length), greaterThan(0));
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Writable Classes
 Writable class hierarchy
<<interface>>
Writable
org.apache.hdaoop.io
Primitives
<<interface>>
WritableComparable
ArrayWritable
BooleanWritable
NullWritable
ByteWritable
Text
IntWritable
BytesWritable
VIntWritable
MD5Hash
FloatWritable
ObjectWritable
LongWritable
GenericWritable
VLongWritable
DoubleWritable
TwoDArrayWritable
AbstractMapWritable
MapWritable
SortedMapWritable
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Others
Writable Wrappers for Java Primitives
 There are Writable wrappers for all the Java primitive types except shot
and char(both of which can be stored in an IntWritable)
 get() for retrieving and set() for storing the wrapped value
 Variable-length formats
– If a value is between -122 and 127, use only a single byte
– Otherwise, use first byte to indicate whether the value is positive or negative
and how many bytes follow
163
VIntWritable: 8fa3
1000 1111 1010 0011
-123
163
(2’s complement)
Java
Primitive
Writable
Implementation
Serialized
Size (bytes)
boolean
BooleanWritable
1
byte
ByteWritable
1
int
IntWritable
4
VIntWritable
1~5
float
FloatWritable
4
long
LongWritable
8
VLongWritable
1~9
DoubleWritable
8
double
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Text
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Writable for UTF-8 sequences
Can be thought of as the Writable equivalent of java.lang.String
Replacement for the org.apache.hadoop.io.UTF8 class (deprecated)
Maximum size is 2GB
Use standard UTF-8
– org.apache.hadoop.io.UTF8 used Java’s modified UTF-8
 Indexing for the Text class is in terms of position in the encoded byte
sequence
 Text is mutable (like all Writable implementations, except NullWritable)
– You can reuse a Text instance by calling one of the set() method
Text t = new Text("hadoop");
t.set("pig");
assertThat(t.getLength(), is(3));
assertThat(t.getBytes().length, is(3));
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Etc.
 BytesWritable
– Wrapper for an array of binary data
 NullWritable
– Zero-length serialization
– Used as a placeholder
– A key or a value can be declared as a NullWritable when you don’t need to
use that position
 ObjectWritable
– General-purpose wrapper for Java primitives, String, enum, Writable, null,
arrays of any of these types
– Useful when a field can be of more than one type
 Writable collections
–
–
–
–
ArrayWritable
TwoDArrayWritable
MapWritable
SortedMapWritable
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Serialization Frameworks
 Using Writable is not mandated by MapReduce API
 Only requirement
– Mechanism that translates to and from a binary representation of each type
 Hadoop has an API for pluggable serialization frameworks
 A serialization framework is represented by an implementation of
Serialization (in org.apache.hadoop.io.serializer package)
 A Serialization defines a mapping from types to Serializer instances
and Deserializer instances
 Set the io.serializations property to a comma-separated list of
classnames to register Serialization implementations
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SequenceFile
 Persistent data structure for binary key-value pairs
 Usage example
– Binary log file
 Key: timestamp
 Value: log
– Container for smaller files
 The keys and values stored in a SequenceFile do not necessarily need
to be Writable
 Any types that can be serialized and deserialized by a Serialization may
be used
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Writing a SequenceFile
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Reading a SequenceFile
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Sync Point
 Point in the stream which can be used to resynchronize with a record
boundary if the reader is “lost”—for example, after seeking to an
arbitrary position in the stream
 sync(long position)
– Position the reader at the next sync point after position
 Do not confuse with sync() method defined by the Syncable interface
for synchronizing buffers to the underlying device
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SequenceFile Format
 Header contains the version number, the names of the key and value
classes, compression details, user-defined metadata, and the sync
marker
 Record format
– No compression
– Record compression
– Block compression
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MapFile
 Sorted SequenceFile with an index to permit lookups by key
 Keys must be instances of WritableComparable and values must be
Writable
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Reading a MapFile
 Call the next() method until it returns false
 Random access lookup can be performed by calling the get() method
– Read the index file into memory
– Perform a binary search on the in-memory index
 Very large MapFile index
– Reindex to change the index interval
– Load only a fraction of the index keys into memory by setting the
io.map.index.skip property
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