The process of collecting garbage typically includes marking, sweeping, and compacting phases, but there can be exceptions for different collectors. The JVM garbage collectors include traditional ones like Serial GC, Parallel GC, Concurrent Mark Sweep (CMS) GC, Garbage First Garbage Collector (G1 GC), and several new ones like Zing/C4, Shenandoah, and ZGC. If this area is about to be full, there will be a GC, which is still counted as a major GC. It is not for objects that survived from the Old Generation to stay permanently. The Permanent Generation stores classes or interned character strings. Eventually, the Old Generation needs a major garbage collection, which can be either a full or partial “Stop the World” event depending on the type of garbage collection configured in the JVM program arguments. Typically, a threshold is set for each Young Generation object, and when that age is met, the object gets moved to the Old Generation. The Old Generation is used to store long-surviving objects. All minor garbage collections are “Stop the World” events, meaning that the JVM stops all application threads until the operation completes. When the Young Generation fills up, using its entire allocated memory, a minor garbage collection occurs. The Young Generation is where all new objects are allocated and aged, meaning their time in existence is monitored. The JVM heap consists of smaller parts or generations: Young Generation, Old Generation, and Permanent Generation. The JVM garbage collection process looks at heap memory, identifies which objects are in use and which are not, and deletes the unused objects to reclaim memory that can be leveraged for other purposes. It translates the instructions from its running programs into instructions and commands that run on the local operating system. The JVM runs on a local machine and functions as an operating system to the Java programs written to execute in it. Along the road, it helped us develop best practices around tuning the JVM for our scale which we hope others in the community will find useful. Preserving the reliability and performance of our internal data services required tuning the GC parameters and memory sizes and reducing the rate at which the system generated Java objects. Refining this core area of our data platform ensures that decision-makers within Uber get actionable business intelligence in a timely manner, letting us deliver the best possible services for our users, whether it’s connecting riders with drivers, restaurants with delivery people, or freight shippers with carriers. Increased memory consumption exposed a variety of issues, including long garbage collection ( GC ) pauses, memory corruption, out-of-memory ( OOM ) exceptions, and memory leaks. Uber’s growth over the last few years exponentially increased both the volume of data and the associated access loads required to process it, resulting in much more memory consumption from services. All of these services were built in Java or Scala and run on open source Java Virtual Machine (JVM). Our data platform leverages several open source projects (Apache Hive, Apache Presto, and Apache Spark ) for both interactive and long running queries, serving the myriad needs of different teams at Uber. Our Apache Hadoop-based data platform ingests hundreds of petabytes of analytical data with minimum latency and stores it in a data lake built on top of the Hadoop Distributed File System (HDFS). Try (RandomAccessFile reader = new RandomAccessFile("src/test/resources/test_read.in", "r") įileChannel channel = reader.Running queries on Uber’s data platform lets us make data -driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks in the driver sign-up process. This test reads the file and checks it was read ok: void givenFile_whenReadWithFileChannelUsingRandomAccessFile_thenCorrect() Let's understand how to read a file using FileChannel on a file that contains: Hello world
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