Tuesday, April 5, 2011

Understanding Greenplum

This blog will guide you to understand Greenplum that includes what is Greenplum, its architecture, different segments, and its basics in details. In this Greenplum tutorial we will try to understand the capability and the architecture provided by Greenplum

What is Greenplum: Greenplum Database is a massively parallel processing (MPP) database server based on PostgreSQL open-source technology. MPP (also known as a shared nothing architecture) refers to systems with two or more processors which cooperate to carry out an operation - each processor with its own memory, operating system and disks.
Source: Greenplum



Greenplum Database Architecture: Greenplum Database utilizes a shared-nothing MPP (massively parallel processing) architecture. In this architecture, data is automatically partitioned across multiple 'segment' servers, and each 'segment' owns and manages a distinct portion of the overall data. All communication is via a network interconnect -- there is no disk-level sharing or contention to be concerned with (i.e. it is a 'shared-nothing' architecture). The segment servers are able to process every query in a fully parallel manner, use all disk connections simultaneously, and efficiently flow data between segments as query plans dictates
Source: Greenplum


Is Greenplum free: Greenplum is not free to setup for production or to setup cluster. But it has launched a community edition which is free but with pre specified guidelines.

What is Community edition: The EMC Greenplum Community Edition (CE) provides a powerful and comprehensive analytic environment enabling users to turn increasingly large amounts of data into useful insight. Developers, data scientists and other data professionals can experiment with real-world data, perform advanced analytics and most importantly - rapidly reveal insights from big data sets with ease

Parallel Query Optimizer: "Greenplum Database's parallel query optimizer is responsible for converting SQL or MapReduce into a physical execution plan." It does this using a cost-based optimization algorithm in which it evaluates a vast number of potential plans and selects the one that it believes will lead to the most efficient query execution.


Parallel Dataflow Engine:  At the heart of the Greenplum Database is the Parallel Dataflow Engine. This is where the real work of processing and analyzing data is done. Greenplum’s Parallel Dataflow Engine is highly optimized at executing both SQL and MapReduce, and does so in a massively parallel manner

 Source: Greenplum


Greenplum Database support (hardware requirements): Greenplum Database supported for production use on SUSE Linux Enterprise Server 10.2 (64-bit),  Red Hat Enterprise Linux 5.x (64-bit), CentOS Linux 5.x (64-bit) and Sun Solaris 10U5+ (64-bit). Greenplum Database 3.3 is supported on server hardware from a range of vendors including HP, Dell, Sun and IBM. Greenplum Database is supported for non-production (development and evaluation) use on Mac OSX 10.5, Red Hat Enterprise Linux 5.2 or higher (32-bit) and CentOS Linux 5.2 or higher (32-bit).

Greenplum Master Segment: "The master is the entry point to the Greenplum Database system. It is the database process that accepts client connections and processes the SQL commands issued by the users of the system". The master is where the global system catalog resides (the set of system tables that contain metadata about the Greenplum Database system itself), however the master does not contain any user data. Data resides only on the segments. The master does the work of authenticating client connections, processing the incoming SQL commands, distributing the work load between the segments, coordinating the results returned by each of the segments, and presenting the final results to the client program

Greenplum Primary Segments: In Greenplum Database, the segments are where the data is stored and where the majority of query processing takes place. User-defined tables and their indexes are distributed across the available number of segments in the Greenplum Database system, each segment containing a distinct portion of the data. Segment instances are the database server processes that serve segments. Users do not interact directly with the segments in a Greenplum Database system, but do so through the master.

Mirror Segment: Mirror segments allow database queries to fail over to a backup segment if the primary segment becomes unavailable. Mirror / secondary segment always resides on a different host than its primary

Source: Greenplum





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