Hadoop

refer to: http://hadoop.apache.org/

What Is Apache Hadoop?

The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

The project includes these modules:

  • Hadoop Common: The common utilities that support the other Hadoop modules.
  • Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
  • Hadoop YARN: A framework for job scheduling and cluster resource management.
  • Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.

Other Hadoop-related projects at Apache include:

  • Ambari™: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which includes support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heatmaps and ability to view MapReduce, Pig and Hive applications visually alongwith features to diagnose their performance characteristics in a user-friendly manner.
  • Avro™: A data serialization system.
  • Cassandra™: A scalable multi-master database with no single points of failure.
  • Chukwa™: A data collection system for managing large distributed systems.
  • HBase™: A scalable, distributed database that supports structured data storage for large tables.
  • Hive™: A data warehouse infrastructure that provides data summarization and ad hoc querying.
  • Mahout™: A Scalable machine learning and data mining library.
  • Pig™: A high-level data-flow language and execution framework for parallel computation.
  • Spark™: A fast and general compute engine for Hadoop data. Spark provides a simple and expressive programming model that supports a wide range of applications, including ETL, machine learning, stream processing, and graph computation.
  • Tez™: A generalized data-flow programming framework, built on Hadoop YARN, which provides a powerful and flexible engine to execute an arbitrary DAG of tasks to process data for both batch and interactive use-cases. Tez is being adopted by Hive™, Pig™ and other frameworks in the Hadoop ecosystem, and also by other commercial software (e.g. ETL tools), to replace Hadoop™ MapReduce as the underlying execution engine.
  • ZooKeeper™: A high-performance coordination service for distributed applications.

Getting Started

To get started, begin here:

  1. Learn about Hadoop by reading the documentation.
  2. Download Hadoop from the release page.
  3. Discuss Hadoop on the mailing list.

Memcached

Refer to: http://www.memcached.org/about

 

About Memcached

memcached is a high-performance, distributed memory object caching system, generic in nature, but originally intended for use in speeding up dynamic web applications by alleviating database load.

You can think of it as a short-term memory for your applications.

What it Does

usage

memcached allows you to take memory from parts of your system where you have more than you need and make it accessible to areas where you have less than you need.

memcached also allows you to make better use of your memory. If you consider the diagram to the right, you can see two deployment scenarios:

  1. Each node is completely independent (top).
  2. Each node can make use of memory from other nodes (bottom).

The first scenario illustrates the classic deployment strategy, however you’ll find that it’s both wasteful in the sense that the total cache size is a fraction of the actual capacity of your web farm, but also in the amount of effort required to keep the cache consistent across all of those nodes.

With memcached, you can see that all of the servers are looking into the same virtual pool of memory. This means that a given item is always stored and always retrieved from the same location in your entire web cluster.

Also, as the demand for your application grows to the point where you need to have more servers, it generally also grows in terms of the data that must be regularly accessed. A deployment strategy where these two aspects of your system scale together just makes sense.

The illustration to the right only shows two web servers for simplicity, but the property remains the same as the number increases. If you had fifty web servers, you’d still have a usable cache size of 64MB in the first example, but in the second, you’d have 3.2GB of usable cache.

Of course, you aren’t required to use your web server’s memory for cache. Many memcached users have dedicated machines that are built to only be memcached servers.

remove request to s.w.org

Add the following codes at the end of functions.php:
remove_action( ‘wp_head’, ‘print_emoji_detection_script’, 7 );
remove_action( ‘admin_print_scripts’, ‘print_emoji_detection_script’ );
remove_action( ‘wp_print_styles’, ‘print_emoji_styles’ );
remove_action( ‘admin_print_styles’, ‘print_emoji_styles’ );