http://git-wip-us.apache.org/repos/asf/flink-web/blob/e5efd40a/content/news/2015/05/11/Juggling-with-Bits-and-Bytes.html ---------------------------------------------------------------------- diff --git a/content/news/2015/05/11/Juggling-with-Bits-and-Bytes.html b/content/news/2015/05/11/Juggling-with-Bits-and-Bytes.html index 234c26d..a4d2561 100644 --- a/content/news/2015/05/11/Juggling-with-Bits-and-Bytes.html +++ b/content/news/2015/05/11/Juggling-with-Bits-and-Bytes.html @@ -164,7 +164,7 @@ However, this approach has a few notable drawbacks. First of all it is not trivi <img src="/img/blog/memory-mgmt.png" style="width:90%;margin:15px" /> </center> -<p>Flinkâs style of active memory management and operating on binary data has several benefits: </p> +<p>Flinkâs style of active memory management and operating on binary data has several benefits:</p> <ol> <li><strong>Memory-safe execution & efficient out-of-core algorithms.</strong> Due to the fixed amount of allocated memory segments, it is trivial to monitor remaining memory resources. In case of memory shortage, processing operators can efficiently write larger batches of memory segments to disk and later them read back. Consequently, <code>OutOfMemoryErrors</code> are effectively prevented.</li> @@ -173,13 +173,13 @@ However, this approach has a few notable drawbacks. First of all it is not trivi <li><strong>Efficient binary operations & cache sensitivity.</strong> Binary data can be efficiently compared and operated on given a suitable binary representation. Furthermore, the binary representations can put related values, as well as hash codes, keys, and pointers, adjacently into memory. This gives data structures with usually more cache efficient access patterns.</li> </ol> -<p>These properties of active memory management are very desirable in a data processing systems for large-scale data analytics but have a significant price tag attached. Active memory management and operating on binary data is not trivial to implement, i.e., using <code>java.util.HashMap</code> is much easier than implementing a spillable hash-table backed by byte arrays and a custom serialization stack. Of course Apache Flink is not the only JVM-based data processing system that operates on serialized binary data. Projects such as <a href="http://drill.apache.org/";>Apache Drill</a>, <a href="http://ignite.incubator.apache.org/";>Apache Ignite (incubating)</a> or <a href="http://projectgeode.org/";>Apache Geode (incubating)</a> apply similar techniques and it was recently announced that also <a href="http://spark.apache.org/";>Apache Spark</a> will evolve into this direction with <a href="https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html";> Project Tungsten</a>. </p> +<p>These properties of active memory management are very desirable in a data processing systems for large-scale data analytics but have a significant price tag attached. Active memory management and operating on binary data is not trivial to implement, i.e., using <code>java.util.HashMap</code> is much easier than implementing a spillable hash-table backed by byte arrays and a custom serialization stack. Of course Apache Flink is not the only JVM-based data processing system that operates on serialized binary data. Projects such as <a href="http://drill.apache.org/";>Apache Drill</a>, <a href="http://ignite.incubator.apache.org/";>Apache Ignite (incubating)</a> or <a href="http://projectgeode.org/";>Apache Geode (incubating)</a> apply similar techniques and it was recently announced that also <a href="http://spark.apache.org/";>Apache Spark</a> will evolve into this direction with <a href="https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html";> Project Tungsten</a>.</p> <p>In the following we discuss in detail how Flink allocates memory, de/serializes objects, and operates on binary data. We will also show some performance numbers comparing processing objects on the heap and operating on binary data.</p> <h2 id="how-does-flink-allocate-memory">How does Flink allocate memory?</h2> -<p>A Flink worker, called TaskManager, is composed of several internal components such as an actor system for coordination with the Flink master, an IOManager that takes care of spilling data to disk and reading it back, and a MemoryManager that coordinates memory usage. In the context of this blog post, the MemoryManager is of most interest. </p> +<p>A Flink worker, called TaskManager, is composed of several internal components such as an actor system for coordination with the Flink master, an IOManager that takes care of spilling data to disk and reading it back, and a MemoryManager that coordinates memory usage. In the context of this blog post, the MemoryManager is of most interest.</p> <p>The MemoryManager takes care of allocating, accounting, and distributing MemorySegments to data processing operators such as sort and join operators. A <a href="https://github.com/apache/flink/blob/release-0.9.0-milestone-1/flink-core/src/main/java/org/apache/flink/core/memory/MemorySegment.java";>MemorySegment</a> is Flinkâs distribution unit of memory and is backed by a regular Java byte array (size is 32 KB by default). A MemorySegment provides very efficient write and read access to its backed byte array using Javaâs unsafe methods. You can think of a MemorySegment as a custom-tailored version of Javaâs NIO ByteBuffer. In order to operate on multiple MemorySegments like on a larger chunk of consecutive memory, Flink uses logical views that implement Javaâs <code>java.io.DataOutput</code> and <code>java.io.DataInput</code> interfaces.</p> @@ -191,7 +191,7 @@ However, this approach has a few notable drawbacks. First of all it is not trivi <h2 id="how-does-flink-serialize-objects">How does Flink serialize objects?</h2> -<p>The Java ecosystem offers several libraries to convert objects into a binary representation and back. Common alternatives are standard Java serialization, <a href="https://github.com/EsotericSoftware/kryo";>Kryo</a>, <a href="http://avro.apache.org/";>Apache Avro</a>, <a href="http://thrift.apache.org/";>Apache Thrift</a>, or Googleâs <a href="https://github.com/google/protobuf";>Protobuf</a>. Flink includes its own custom serialization framework in order to control the binary representation of data. This is important because operating on binary data such as comparing or even manipulating binary data requires exact knowledge of the serialization layout. Further, configuring the serialization layout with respect to operations that are performed on binary data can yield a significant performance boost. Flinkâs serialization stack also leverages the fact, that the type of the objects which are going through de/serialization are exactly known before a program is executed. </p> +<p>The Java ecosystem offers several libraries to convert objects into a binary representation and back. Common alternatives are standard Java serialization, <a href="https://github.com/EsotericSoftware/kryo";>Kryo</a>, <a href="http://avro.apache.org/";>Apache Avro</a>, <a href="http://thrift.apache.org/";>Apache Thrift</a>, or Googleâs <a href="https://github.com/google/protobuf";>Protobuf</a>. Flink includes its own custom serialization framework in order to control the binary representation of data. This is important because operating on binary data such as comparing or even manipulating binary data requires exact knowledge of the serialization layout. Further, configuring the serialization layout with respect to operations that are performed on binary data can yield a significant performance boost. Flinkâs serialization stack also leverages the fact, that the type of the objects which are going through de/serialization are exactly known before a program is executed.</p> <p>Flink programs can process data represented as arbitrary Java or Scala objects. Before a program is optimized, the data types at each processing step of the programâs data flow need to be identified. For Java programs, Flink features a reflection-based type extraction component to analyze the return types of user-defined functions. Scala programs are analyzed with help of the Scala compiler. Flink represents each data type with a <a href="https://github.com/apache/flink/blob/release-0.9.0-milestone-1/flink-core/src/main/java/org/apache/flink/api/common/typeinfo/TypeInformation.java";>TypeInformation</a>. Flink has TypeInformations for several kinds of data types, including:</p> @@ -201,11 +201,11 @@ However, this approach has a few notable drawbacks. First of all it is not trivi <li>WritableTypeInfo: Any implementation of Hadoopâs Writable interface.</li> <li>TupleTypeInfo: Any Flink tuple (Tuple1 to Tuple25). Flink tuples are Java representations for fixed-length tuples with typed fields.</li> <li>CaseClassTypeInfo: Any Scala CaseClass (including Scala tuples).</li> - <li>PojoTypeInfo: Any POJO (Java or Scala), i.e., an object with all fields either being public or accessible through getters and setter that follow the common naming conventions. </li> + <li>PojoTypeInfo: Any POJO (Java or Scala), i.e., an object with all fields either being public or accessible through getters and setter that follow the common naming conventions.</li> <li>GenericTypeInfo: Any data type that cannot be identified as another type.</li> </ul> -<p>Each TypeInformation provides a serializer for the data type it represents. For example, a BasicTypeInfo returns a serializer that writes the respective primitive type, the serializer of a WritableTypeInfo delegates de/serialization to the write() and readFields() methods of the object implementing Hadoopâs Writable interface, and a GenericTypeInfo returns a serializer that delegates serialization to Kryo. Object serialization to a DataOutput which is backed by Flink MemorySegments goes automatically through Javaâs efficient unsafe operations. For data types that can be used as keys, i.e., compared and hashed, the TypeInformation provides TypeComparators. TypeComparators compare and hash objects and can - depending on the concrete data type - also efficiently compare binary representations and extract fixed-length binary key prefixes. </p> +<p>Each TypeInformation provides a serializer for the data type it represents. For example, a BasicTypeInfo returns a serializer that writes the respective primitive type, the serializer of a WritableTypeInfo delegates de/serialization to the write() and readFields() methods of the object implementing Hadoopâs Writable interface, and a GenericTypeInfo returns a serializer that delegates serialization to Kryo. Object serialization to a DataOutput which is backed by Flink MemorySegments goes automatically through Javaâs efficient unsafe operations. For data types that can be used as keys, i.e., compared and hashed, the TypeInformation provides TypeComparators. TypeComparators compare and hash objects and can - depending on the concrete data type - also efficiently compare binary representations and extract fixed-length binary key prefixes.</p> <p>Tuple, Pojo, and CaseClass types are composite types, i.e., containers for one or more possibly nested data types. As such, their serializers and comparators are also composite and delegate the serialization and comparison of their member data types to the respective serializers and comparators. The following figure illustrates the serialization of a (nested) <code>Tuple3<Integer, Double, Person></code> object where <code>Person</code> is a POJO and defined as follows:</p> @@ -218,13 +218,13 @@ However, this approach has a few notable drawbacks. First of all it is not trivi <img src="/img/blog/data-serialization.png" style="width:80%;margin:15px" /> </center> -<p>Flinkâs type system can be easily extended by providing custom TypeInformations, Serializers, and Comparators to improve the performance of serializing and comparing custom data types. </p> +<p>Flinkâs type system can be easily extended by providing custom TypeInformations, Serializers, and Comparators to improve the performance of serializing and comparing custom data types.</p> <h2 id="how-does-flink-operate-on-binary-data">How does Flink operate on binary data?</h2> <p>Similar to many other data processing APIs (including SQL), Flinkâs APIs provide transformations to group, sort, and join data sets. These transformations operate on potentially very large data sets. Relational database systems feature very efficient algorithms for these purposes since several decades including external merge-sort, merge-join, and hybrid hash-join. Flink builds on this technology, but generalizes it to handle arbitrary objects using its custom serialization and comparison stack. In the following, we show how Flink operates with binary data by the example of Flinkâs in-memory sort algorithm.</p> -<p>Flink assigns a memory budget to its data processing operators. Upon initialization, a sort algorithm requests its memory budget from the MemoryManager and receives a corresponding set of MemorySegments. The set of MemorySegments becomes the memory pool of a so-called sort buffer which collects the data that is be sorted. The following figure illustrates how data objects are serialized into the sort buffer. </p> +<p>Flink assigns a memory budget to its data processing operators. Upon initialization, a sort algorithm requests its memory budget from the MemoryManager and receives a corresponding set of MemorySegments. The set of MemorySegments becomes the memory pool of a so-called sort buffer which collects the data that is be sorted. The following figure illustrates how data objects are serialized into the sort buffer.</p> <center> <img src="/img/blog/sorting-binary-data-1.png" style="width:90%;margin:15px" /> @@ -237,7 +237,7 @@ The following figure shows how two objects are compared.</p> <img src="/img/blog/sorting-binary-data-2.png" style="width:80%;margin:15px" /> </center> -<p>The sort buffer compares two elements by comparing their binary fix-length sort keys. The comparison is successful if either done on a full key (not a prefix key) or if the binary prefix keys are not equal. If the prefix keys are equal (or the sort key data type does not provide a binary prefix key), the sort buffer follows the pointers to the actual object data, deserializes both objects and compares the objects. Depending on the result of the comparison, the sort algorithm decides whether to swap the compared elements or not. The sort buffer swaps two elements by moving their fix-length keys and pointers. The actual data is not moved. Once the sort algorithm finishes, the pointers in the sort buffer are correctly ordered. The following figure shows how the sorted data is returned from the sort buffer. </p> +<p>The sort buffer compares two elements by comparing their binary fix-length sort keys. The comparison is successful if either done on a full key (not a prefix key) or if the binary prefix keys are not equal. If the prefix keys are equal (or the sort key data type does not provide a binary prefix key), the sort buffer follows the pointers to the actual object data, deserializes both objects and compares the objects. Depending on the result of the comparison, the sort algorithm decides whether to swap the compared elements or not. The sort buffer swaps two elements by moving their fix-length keys and pointers. The actual data is not moved. Once the sort algorithm finishes, the pointers in the sort buffer are correctly ordered. The following figure shows how the sorted data is returned from the sort buffer.</p> <center> <img src="/img/blog/sorting-binary-data-3.png" style="width:80%;margin:15px" /> @@ -255,7 +255,7 @@ The following figure shows how two objects are compared.</p> <li><strong>Kryo-serialized.</strong> The tuple fields are serialized into a sort buffer of 600 MB size using Kryo serialization and sorted without binary sort keys. This means that each pair-wise comparison requires two object to be deserialized.</li> </ol> -<p>All sort methods are implemented using a single thread. The reported times are averaged over ten runs. After each run, we call <code>System.gc()</code> to request a garbage collection run which does not go into measured execution time. The following figure shows the time to store the input data in memory, sort it, and read it back as objects. </p> +<p>All sort methods are implemented using a single thread. The reported times are averaged over ten runs. After each run, we call <code>System.gc()</code> to request a garbage collection run which does not go into measured execution time. The following figure shows the time to store the input data in memory, sort it, and read it back as objects.</p> <center> <img src="/img/blog/sort-benchmark.png" style="width:90%;margin:15px" /> @@ -313,13 +313,13 @@ The following figure shows how two objects are compared.</p> <p><br /></p> -<p>To summarize, the experiments verify the previously stated benefits of operating on binary data. </p> +<p>To summarize, the experiments verify the previously stated benefits of operating on binary data.</p> <h2 id="were-not-done-yet">Weâre not done yet!</h2> -<p>Apache Flink features quite a bit of advanced techniques to safely and efficiently process huge amounts of data with limited memory resources. However, there are a few points that could make Flink even more efficient. The Flink community is working on moving the managed memory to off-heap memory. This will allow for smaller JVMs, lower garbage collection overhead, and also easier system configuration. With Flinkâs Table API, the semantics of all operations such as aggregations and projections are known (in contrast to black-box user-defined functions). Hence we can generate code for Table API operations that directly operates on binary data. Further improvements include serialization layouts which are tailored towards the operations that are applied on the binary data and code generation for serializers and comparators. </p> +<p>Apache Flink features quite a bit of advanced techniques to safely and efficiently process huge amounts of data with limited memory resources. However, there are a few points that could make Flink even more efficient. The Flink community is working on moving the managed memory to off-heap memory. This will allow for smaller JVMs, lower garbage collection overhead, and also easier system configuration. With Flinkâs Table API, the semantics of all operations such as aggregations and projections are known (in contrast to black-box user-defined functions). Hence we can generate code for Table API operations that directly operates on binary data. Further improvements include serialization layouts which are tailored towards the operations that are applied on the binary data and code generation for serializers and comparators.</p> -<p>The groundwork (and a lot more) for operating on binary data is done but there is still some room for making Flink even better and faster. If you are crazy about performance and like to juggle with lot of bits and bytes, join the Flink community! </p> +<p>The groundwork (and a lot more) for operating on binary data is done but there is still some room for making Flink even better and faster. If you are crazy about performance and like to juggle with lot of bits and bytes, join the Flink community!</p> <h2 id="tldr-give-me-three-things-to-remember">TL;DR; Give me three things to remember!</h2>
http://git-wip-us.apache.org/repos/asf/flink-web/blob/e5efd40a/content/news/2015/05/14/Community-update-April.html ---------------------------------------------------------------------- diff --git a/content/news/2015/05/14/Community-update-April.html b/content/news/2015/05/14/Community-update-April.html index 67844f1..7df01df 100644 --- a/content/news/2015/05/14/Community-update-April.html +++ b/content/news/2015/05/14/Community-update-April.html @@ -145,7 +145,7 @@ <article> <p>14 May 2015 by Kostas Tzoumas (<a href="https://twitter.com/kostas_tzoumas";>@kostas_tzoumas</a>)</p> -<p>April was an packed month for Apache Flink. </p> +<p>April was an packed month for Apache Flink.</p> <h2 id="flink-090-milestone1-release">Flink 0.9.0-milestone1 release</h2> @@ -161,7 +161,7 @@ <h2 id="flink-on-the-web">Flink on the web</h2> -<p>Fabian Hueske gave an <a href="http://www.infoq.com/news/2015/04/hueske-apache-flink?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global";>interview at InfoQ</a> on Apache Flink. </p> +<p>Fabian Hueske gave an <a href="http://www.infoq.com/news/2015/04/hueske-apache-flink?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global";>interview at InfoQ</a> on Apache Flink.</p> <h2 id="upcoming-events">Upcoming events</h2>
