Serialization is the 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 turning a byte stream back into a series of structured objects.Serialization appears in two quite distinct areas of distributed data processing: for interprocess communication and for persistent storage.
In Hadoop, interprocess communication between nodes in the system is implemented using remote procedure calls (RPCs). The RPC protocol uses serialization to render the message into a binary stream to be sent to the remote node, which then deserializes the binary stream into the original message. In general, it is desirable that an RPC serialization format is:
A compact format makes the best use of network bandwidth, which is the most scarce resource in a data center.
Interprocess communication forms the backbone for a distributed system, so it is essential that there is as little performance overhead as possible for the serialization and deserialization process.
Protocols change over time to meet new requirements, so it should be straightforward to evolve the protocol in a controlled manner for clients and servers. For example, it should be possible to add a new argument to a method call, and have the new servers accept messages in the old format (without the new argument) from old clients.
For some systems, it is desirable to be able to support clients that are written in different languages to the server, so the format needs to be designed to make this possible.
On the face of it, the data format chosen for persistent storage would have different requirements from a serialization framework. After all, the lifespan of an RPC is less than a second, whereas persistent data may be read years after it was written. As it turns out, the four desirable properties of an RPC’s serialization format are also crucial for a persistent storage format. We want the storage format to be compact (to make efficient use of storage space), fast (so the overhead in reading or writing terabytes of data is minimal), extensible (so we can transparently read data written in an older format), and interoperable (so we can read or write persistent data using different languages).
Hadoop uses its own serialization format, Writables, which is certainly compact and fast, but not so easy to extend or use from languages other than Java. Since Writables are central to Hadoop (most MapReduce programs use them for their key and value types), we look at them in some depth in the next three sections, before looking at serialization frameworks in general, and then Avro (a serialization system that was designed to overcome some of the limitations of Writables) in more detail.
The Writable Interface
The Writable interface defines two methods: one for writing its state to a DataOutput binary stream, and one for reading its state from a DataInput binary stream:
Let’s look at a particular Writable to see what we can do with it. We will use IntWritable, a wrapper for a Java int. We can create one and set its value using the set() method:
Equivalently, we can use the constructor that takes the integer value:
To examine the serialized form of the IntWritable, we write a small helper method that wraps a java.io.ByteArrayOutputStream in a java.io.DataOutputStream (an implementation of java.io.DataOutput) to capture the bytes in the serialized stream:
An integer is written using four bytes (as we see using JUnit 4 assertions):
The bytes are written in big-endian order (so the most significant byte is written to the stream first, this is dictated by the java.io.DataOutput interface), and we can see their hexadecimal representation by using a method on Hadoop’s StringUtils:
assertThat(StringUtils.byteToHexString(bytes), is("000000a3"));Let’sdeserialization. Again, we create a helper method to read a Writable objectfrom a byte array:
We construct a new, value-less, IntWritable, then call deserialize() to read from the output data that we just wrote. Then we check that its value, retrieved using the get() method, is the original value, 163:
WritableComparable and comparators
IntWritable implements the WritableComparable interface, which is just a subinterface of the Writable and java.lang.Comparable interfaces:
Comparison of types is crucial for MapReduce, where there is a sorting phase during which keys are compared with one another. One optimization that Hadoop provides is the RawComparator extension of Java’s Comparator:
This interface permits implementors to compare records read from a stream without deserializing them into objects, thereby avoiding any overhead of object creation. For example, the comparator for IntWritables implements the raw compare() method by reading an integer from each of the byte arrays b1 and b2 and comparing them directly, from the given start positions (s1 and s2) and lengths (l1 and l2).
WritableComparator is a general-purpose implementation of RawComparator for WritableComparable classes. It provides two main functions. First, it provides a default implementation of the raw compare() method that deserializes the objects to be comparedfrom the stream and invokes the object compare() method. Second, it acts as a factory for RawComparator instances (that Writable implementations have registered). For example, to obtain a comparator for IntWritable, we just use:
Hadoop comes with a large selection of Writable classes in the org.apache.hadoop.io package. They form the class hierarchy shown in below
Writable wrappers for Java primitives
There are Writable wrappers for all the Java primitive types (see Table ) except short and char (both of which can be stored in an IntWritable). All have a get() and a set() method for retrieving and storing the wrapped value.
When it comes to encoding integers, there is a choice between the fixed-length formats (IntWritable and LongWritable) and the variable-length formats (VIntWritable and VLongWritable). The variable-length formats use only a single byte to encode the value if it is small enough (between –112 and 127, inclusive); otherwise, they use the first byte to indicate whether the value is positive or negative, and how many bytes follow. For example, 163 requires two bytes:
How do you choose between a fixed-length and a variable-length encoding? Fixedlength encodings are good when the distribution of values is fairly uniform across the whole value space, such as a (well-designed) hash function. Most numeric variables tend to have nonuniform distributions, and on average the variable-length encoding will save space. Another advantage of variable-length encodings is that you can switch from VIntWritable to VLongWritable, since their encodings are actually the same. So by choosing a variable-length representation, you have room to grow without committing to an 8-byte long representation from the beginning.
Text is a Writable for UTF-8 sequences. It can be thought of as the Writable equivalent of java.lang.String. Text is a replacement for the UTF8 class, which was deprecatedoding was over 32,767 bytes, and because it used Java’s modified UTF-8.
The Text class uses an int (with a variable-length encoding) to store the number of bytes in the string encoding, so the maximum value is 2 GB. Furthermore, Text uses standard UTF-8, which makes it potentially easier to interoperate with other tools that understand UTF-8.
Because of its emphasis on using standard UTF-8, there are some differences between Text and the Java String class. Indexing for the Text class is in terms of position in the encoded byte sequence, not the Unicode character in the string, or the Java char code unit (as it is for String). For ASCII strings, these three concepts of index position coincide. Here is an example to demonstrate the use of the charAt() method:
All but the last character in the table, U+10400, can be expressed using a single Javachar. U+10400 is a supplementary character and is represented by two Java chars,known as a surrogate pair. The tests in Example 4-5 show the differences betweenString and Text when processing a string of the four characters
Tests showing the differences between the String and Text classes
The test confirms that the length of a String is the number of char code units it contains (5, one from each of the first three characters in the string, and a surrogate pair from the last), whereas the length of a Text object is the number of bytes in its UTF-8 encoding(10 = 1+2+3+4). Similarly, the indexOf() method in String returns an index in char code units, and find() for Text is a byte offset.
The charAt() method in String returns the char code unit for the given index, which in the case of a surrogate pair will not represent a whole Unicode character. The code PointAt() method, indexed by char code unit, is needed to retrieve a single Unicode character represented as an int. In fact, the charAt() method in Text is more like the codePointAt() method than its namesake in String. The only difference is that it is indexed by byte offset.
Iterating over the Unicode characters in Text is complicated by the use of byte offsets for indexing, since you can’t just increment the index. The idiom for iteration is a little obscure (see Example ): turn the Text object into a java.nio.ByteBuffer, then repeatedly call the bytesToCodePoint() static method on Text with the buffer. This method extracts the next code point as an int and updates the position in the buffer. The end of the string is detected when bytesToCodePoint() returns –1.
Iterating over the characters in a Text object
Mutability.Another difference with String is that Text is mutable (like all Writable implementations in Hadoop, except NullWritable, which is a singleton). You can reuse a Text instance by calling one of the set() methods on it. For example:
This shows why it is imperative that you always call getLength() when calling getBytes(), so you know how much of the byte array is valid data.
Resorting to String.Text doesn’t have as rich an API for manipulating strings as java.lang.String, so in many cases, you need to convert the Text object to a String. This is done in the usual way, using the toString() method:
BytesWritable is a wrapper for an array of binary data. Its serialized format is an integer field (4 bytes) that specifies the number of bytes to follow, followed by the bytes themselves. For example, the byte array of length two with values 3 and 5 is serialized as a 4-byte integer (00000002) followed by the two bytes from the array (03 and 05):
BytesWritable is mutable, and its value may be changed by calling its set() method. As with Text, the size of the byte array returned from the getBytes() method for Byte sWritable the capacity may not reflect the actual size of the data stored in the BytesWritable. You can determine the size of the BytesWritable by calling get Length(). To demonstrate:
NullWritable is a special type of Writable, as it has a zero-length serialization. No bytes are written to, or read from, the stream. It is used as a placeholder; for example, in MapReduce, a key or a value can be declared as a NullWritable when you don’t need to use that position it effectively stores a constant empty value. NullWritable can also be useful as a key in SequenceFile when you want to store a list of values, as opposed to key-value pairs. It is an immutable singleton: the instance can be retrieved by calling NullWritable.get().
ObjectWritable and GenericWritable
ObjectWritable is a general-purpose wrapper for the following: Java primitives, String, enum, Writable, null, or arrays of any of these types. It is used in Hadoop RPC to marshal and unmarshal method arguments and return types.
ObjectWritable is useful when a field can be of more than one type: for example, if the values in a SequenceFile have multiple types, then you can declare the value type as an ObjectWritable and wrap each type in an ObjectWritable. Being a general-purpose mechanism, it’s fairly wasteful of space since it writes the classname of the wrapped type every time it is serialized. In cases where the number of types is small and known ahead of time, this can be improved by having a static array of types, and using the index into the array as the serialized reference to the type. This is the approach that GenericWritable takes, and you have to subclass it to specify the types to support.
There are four Writable collection types in the org.apache.hadoop.io package: Array Writable, TwoDArrayWritable, MapWritable, and SortedMapWritable. ArrayWritable and TwoDArrayWritable are Writable implementations for arrays and two-dimensional arrays (array of arrays) of Writable instances. All the elements of an ArrayWritable or a TwoDArrayWritable must be instances of the same class, which is specified at construction, as follows:
In contexts where the Writable is defined by type, such as in SequenceFile keys or values, or as input to MapReduce in general, you need to subclass ArrayWritable (or TwoDArrayWritable, as appropriate) to set the type statically. For example:
ArrayWritable and TwoDArrayWritable both have get() and set() methods, as well as a toArray() method, which creates a shallow copy of the array (or 2D array).
MapWritable and SortedMapWritable are implementations of java.util.Map<Writable, Writable> and java.util.SortedMap<WritableComparable, Writable>, respectively. The type of each key and value field is a part of the serialization format for that field. The type is stored as a single byte that acts as an index into an array of types. The array is populated with the standard types in the org.apache.hadoop.io package, but custom Writable types are accommodated, too, by writing a header that encodes the type array for nonstandard types. As they are implemented, MapWritable and SortedMapWritable use positive byte values for custom types, so a maximum of 127 distinct nonstandard Writable classes can be used in any particular MapWritable or SortedMapWritable instance. Here’s a demonstration of using a MapWritable with different types for keys and values:
Conspicuous by their absence are Writable collection implementations for sets and lists. A set can be emulated by using a MapWritable (or a SortedMapWritable for a sorted set), with NullWritable values. For lists of a single type of Writable, ArrayWritable is adequate, but to store different types of Writable in a single list, you can use GenericWritable to wrap the elements in an ArrayWritable. Alternatively, you could write a general ListWritable using the ideas from MapWritable.
Implementing a Custom Writable
Hadoop comes with a useful set of Writable implementations that serve most purposes; however, on occasion, you may need to write your own custom implementation. With a custom Writable, you have full control over the binary representation and the sort order. Because Writables are at the heart of the MapReduce data path, tuning the binary representation can have a significant effect on performance. The stock Writable implementations that come with Hadoop are well-tuned, but for more elaborate structures, it is often better to create a new Writable type, rather than compose the stock types.
To demonstrate how to create a custom Writable, we shall write an implementation that represents a pair of strings, called TextPair. The basic implementation is shown in below:
A Writable implementation that stores a pair of Text objects
The first part of the implementation is straightforward: there are two Text instance variables, first and second, and associated constructors, getters, and setters. All Writable implementations must have a default constructor so that the MapReduce framework can instantiate them, then populate their fields by calling readFields(). Writable instances are mutable and often reused, so you should take care to avoidallocating objects in the write() or readFields() methods.
TextPair’s write() method serializes each Text object in turn to the output stream, by delegating to the Text objects themselves. Similarly, readFields() deserializes the bytes from the input stream by delegating to each Text object. The DataOutput and DataInput interfaces have a rich set of methods for serializing and deserializing Java primitives, so, in general, you have complete control over the wire format of your Writable object.
Just as you would for any value object you write in Java, you should override the hashCode(), equals(), and toString() methods from java.lang.Object. The hash Code() method is used by the HashPartitioner (the default partitioner in MapReduce) to choose a reduce partition, so you should make sure that you write a good hash function that mixes well to ensure reduce partitions are of a similar size.
If you ever plan to use your custom Writable with TextOutputFormat, then you must implement its toString() method. TextOutputFormat calls toString() on keys and values for their output representation. For Text Pair, we write the underlying Text objects as strings separated by a tab character.
TextPair is an implementation of WritableComparable, so it provides an implementation of the compareTo() method that imposes the ordering you would expect: it sorts by the first string followed by the second. Notice that TextPair differs from TextArrayWrita ble from the previous section (apart from the number of Text objects it can store), since TextArrayWritable is only a Writable, not a WritableComparable.
Implementing a RawComparator for speed
The code for TextPair in Example will work as it stands; however, there is a further optimization we can make. As explained in “WritableComparable and comparators” on page 88, when TextPair is being used as a key in MapReduce, it will have to be deserialized into an object for the compareTo() method to be invoked. What if it were possible to compare two TextPair objects just by looking at their serialized representations?
It turns out that we can do this, since TextPair is the concatenation of two Text objects, and the binary representation of a Text object is a variable-length integer containing the number of bytes in the UTF-8 representation of the string, followed by the UTF-8 bytes themselves. The trick is to read the initial length, so we know how long the first Text object’s byte representation is; then we can delegate to Text’s RawComparator, and invoke it with the appropriate offsets for the first or second string. gives the details (note that this code is nested in the TextPair class).
A RawComparator for comparing TextPair byte representations
We actually subclass WritableComparator rather than implement RawComparator directly, since it provides some convenience methods and default implementations. The subtle part of this code is calculating firstL1 and firstL2, the lengths of the first Text field in each byte stream. Each is made up of the length of the variable-length integer (returned by decodeVIntSize() on WritableUtils) and the value it is encoding (returned by readVInt()).
The static block registers the raw comparator so that whenever MapReduce sees the TextPair class, it knows to use the raw comparator as its default comparator.
As we can see with TextPair, writing raw comparators takes some care, since you have to deal with details at the byte level. It is worth looking at some of the implementations of Writable in the org.apache.hadoop.io package for further ideas, if you need to writeyour own. The utility methods on WritableUtils are very handy, too.
Custom comparators should also be written to be RawComparators, if possible. These are comparators that implement a different sort order to the natural sort order defined by the default comparator. shows a comparator for TextPair, called First Comparator, that considers only the first string of the pair. Note that we override the compare() method that takes objects so both compare() methods have the same semantics.We will make use of this comparator, when we look at joins and secondary sorting in MapReduce.
A custom RawComparator for comparing the first field of TextPair byte representations
Although most MapReduce programs use Writable key and value types, this isn’t mandated by the MapReduce API. In fact, any types can be used; the only requirement is that there be a mechanism that translates to and from a binary representation of each type.
To support this, Hadoop has an API for pluggable serialization frameworks. A serialization framework is represented by an implementation of Serialization (in the org.apache.hadoop.io.serializer package). WritableSerialization, for example, is the implementation of Serialization for Writable types.A Serialization defines a mapping from types to Serializer instances (for turning an object into a byte stream) and Deserializer instances (for turning a byte stream into an object).
Set the io.serializations property to a comma-separated list of classnames to register Serialization implementations. Its default value is org.apache.hadoop.io.serial izer.WritableSerialization, which means that only Writable objects can be serialized or deserialized out of the box.
Hadoop includes a class called JavaSerialization that uses Java Object Serialization. Although it makes it convenient to be able to use standard Java types in MapReduce programs, like Integer or String, Java Object Serialization is not as efficient as Writables, so it’s not worth making this trade-off (see the sidebar on the next page).
Why Not Use Java Object Serialization?
Java comes with its own serialization mechanism, called Java Object Serialization (often referred to simply as “Java Serialization”), that is tightly integrated with the language, so it’s natural to ask why this wasn’t used in Hadoop. Here’s what Doug Cutting said in response to that question:Why didn’t I use Serialization when we first started Hadoop? Because it looked big and hairy and I thought we needed something lean and mean, where we had precise control over exactly how objects are written and read, since that is central to Hadoop. With Serialization you can get some control, but you have to fight for it.
The logic for not using RMI was similar. Effective, high-performance inter-process communications are critical to Hadoop. I felt like we’d need to precisely control how things like connections, timeouts and buffers are handled, and RMI gives you little control over those.The problem is that Java Serialization doesn’t meet the criteria for a serialization format listed earlier: compact, fast, extensible, and interoperable.
Java Serialization is not compact: it writes the classname of each object being written to the stream this is true of classes that implement java.io.Serializable or java.io.Externalizable. Subsequent instances of the same class write a reference handle to the first occurrence, which occupies only 5 bytes. However, reference handles don’t work well with random access, since the referent class may occur at any point in the preceding stream that is, there is state stored in the stream. Even worse, reference handles play havoc with sorting records in a serialized stream, since the first record of a particular class is distinguished and must be treated as a special case.
All these problems are avoided by not writing the classname to the stream at all, which is the approach that Writable takes. This makes the assumption that the client knows the expected type. The result is that the format is considerably more compact than Java Serialization, and random access and sorting work as expected since each record is independent of the others (so there is no stream state).
Java Serialization is a general-purpose mechanism for serializing graphs of objects, so it necessarily has some overhead for serialization and deserialization operations. What’s more, the deserialization procedure creates a new instance for each object deserialized from the stream. Writable objects, on the other hand, can be (and often are) reused. For example, for a MapReduce job, which at its core serializes and deserializes billions of records of just a handful of different types, the savings gained by not having to allocate new objects are significant.
In terms of extensibility, Java Serialization has some support for evolving a type, but it is brittle and hard to use effectively (Writables have no support: the programmer has to manage them himself).In principle, other languages could interpret the Java Serialization stream protocol (defined by the Java Object Serialization Specification), but in practice there are no widely used implementations in other languages, so it is a Java-only solution. The situation is the same for Writables.
There are a number of other serialization frameworks that approach the problem in a different way: rather than defining types through code, you define them in a languageneutral, declarative fashion, using an interface description language (IDL). The system can then generate types for different languages, which is good for interoperability. They also typically define versioning schemes that make type evolution straightforward.Hadoop’s own Record I/O (found in the org.apache.hadoop.record package) has an IDL that is compiled into Writable objects, which makes it convenient for generating types that are compatible with MapReduce. For whatever reason, however, Record I/O was not widely used, and has been deprecated in favor of Avro.
Apache Thrift and Google Protocol Buffers are both popular serialization frameworks, and they are commonly used as a format for persistent binary data. There is limited support for these as MapReduce formats; however, Thrift is used in parts of Hadoop to provide cross-language APIs, such as the “thriftfs” contrib module, where it is used to expose an API to Hadoop filesystems .
In the next section, we look at Avro, an IDL-based serialization framework designed to work well with large-scale data processing in Hadoop.
Apache Avro# is a language-neutral data serialization system. The project was created
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The Hadoop Distributed Filesystem
Developing A Mapreduce Application
How Mapreduce Works
Mapreduce Types And Formats
Setting Up A Hadoop Cluster
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