Shard (database architecture) Some data within a database remains present in all shards,[notes 1] but some only appears in a single shard. Each shard (or server) acts as the single source for this subset of data.[1] A heavier reliance on the interconnect between servers[citation needed]Increased latency when querying, especially where more than one shard must be searched. [citation needed]Data or indexes are often only sharded one way, so that some searches are optimal, and others are slow or impossible. [clarification needed]Issues of consistency and durability due to the more complex failure modes of a set of servers, which often result in systems making no guarantees about cross-shard consistency or durability. [citation needed] In practice, sharding is complex.
Although it has been done for a long time by hand-coding (especially where rows have an obvious grouping, as per the example above), this is often inflexible. This makes replication across multiple servers easy (simple horizontal partitioning does not). Shared nothing architecture. A shared nothing architecture (SN) is a distributed computing architecture in which each node is independent and self-sufficient, and there is no single point of contention across the system. More specifically, none of the nodes share memory or disk storage. Shared nothing is popular for web development because of its scalability. As Google has demonstrated, a pure SN system can scale almost infinitely simply by adding nodes in the form of inexpensive computers, since there is no single bottleneck to slow the system down.[4] Google calls this sharding.
A SN system typically partitions its data among many nodes on different databases (assigning different computers to deal with different users or queries), or may require every node to maintain its own copy of the application's data, using some kind of coordination protocol. This is often referred to as database sharding.
Shared nothing architectures have become prevalent in the data warehousing space. What is shared? See also[edit] Data warehouse. Data Warehouse Overview In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a database used for reporting and data analysis. Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.
The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). A data warehouse constructed from integrated data source systems does not require ETL, staging databases, or operational data store databases. A data mart is a small data warehouse focused on a specific area of interest. This definition of the data warehouse focuses on data storage.
Benefits of a data warehouse[edit] A data warehouse maintains a copy of information from the source transaction systems. History[edit] Massively parallel (computing) In computing, massively parallel refers to the use of a large number of processors (or separate computers) to perform a set of coordinated computations in parallel. In one approach, e.g., in grid computing the processing power of a large number of computers in distributed, diverse administrative domains, is opportunistically used whenever a computer is available.[1] An example is BOINC, a volunteer-based, opportunistic grid system.[2] In another approach, a large number of processors are used in close proximity to each other, e.g., in a computer cluster.
In such a centralized system the speed and flexibility of the interconnect becomes very important, and modern supercomputers have used various approaches ranging from enhanced Infiniband systems to three-dimensional torus interconnects.[3] The term also applies to massively parallel processor arrays (MPPAs) a type of integrated circuit with an array of hundreds or thousands of CPUs and RAM banks. Greenplum. Greenplum was a big data analytics company headquartered in San Mateo, California.[1][2] Greenplum's products include its Unified Analytics Platform, Data Computing Appliance, Analytics Lab, Database, HD and Chorus. Greenplum was acquired by EMC Corporation in July 2010,[3] and then became part of GoPivotal in 2012.[4] Company[edit] Greenplum was founded in September 2003 by Pramukh and Luke Lonergan.[5] It was a merger of two smaller companies Metapa in Los Angeles and Didera in Fairfax, Virginia.[6] Investors included SoundView Ventures, Hudson Ventures and Royal Wulff Ventures.
A total of $20 million in funding was announced at the merger.[7] Greenplum, based in in San Mateo, California, released its database management system software in April 2005 calling it Bizgres.[8] In July 2006 a partnership with Sun Microsystems was announced.[9] Greenplum was acquired by EMC Corporation in July 2010,[3] becoming the foundation of EMC's Big Data Division. Technology[edit] See also[edit] SQL vs. NoSQL: Which Is Better?