Sql Query Language Reference

With the proliferation of data volume, Mysql+memcache has not met the needs of large-scale Internet applications, many organizations have chosen Redis as its architectural complement, however, redis the use of the threshold is not low, such as not supporting SQL, here for everyone to share the Redis use of the full raiders. Redis, one of the most closely watched NoSQL databases, has been used by many well-known internet companies, such as Sina Weibo, Pinterest and Viacom. However, being born with no support for SQL makes him look difficult ...

Htsql is a URI based http://www.aliyun.com/zixun/aggregation/22.html "> relational database Advanced Query language that encapsulates database access using the WEB service layer and converts HTTP requests to SQL Command and returns the query result, formatted as HTML or JSON. Htsql 2.1.0-rc1 Update log: Added/:TSV formatter t ...

Hive on Mapreduce Hive on Mapreduce execution Process Execution process detailed parsing step 1:ui (user interface) invokes ExecuteQuery interface, sending HQL query to Driver step 2:driver Create a session handle for the query statement and send the query statement to Compiler for statement resolution and build execution Plan step 3 and 4:compil ...

Apache Pig, a high-level query language for large-scale data processing, works with Hadoop to achieve a multiplier effect when processing large amounts of data, up to N times less than it is to write large-scale data processing programs in languages ​​such as Java and C ++ The same effect of the code is also small N times. Apache Pig provides a higher level of abstraction for processing large datasets, implementing a set of shell scripts for the mapreduce algorithm (framework) that handle SQL-like data-processing scripting languages ​​in Pig ...

Hadoop is a highly scalable, large data application that can handle dozens of TB to hundreds of PB of data through fewer than thousands of interconnected servers. This reference design realizes a single cabinet of Hadoop cluster design, if users need more than one cabinet of Hadoop cluster, can expand the design of the number of servers and network bandwidth easy to achieve expansion. Hadoop solution The features of Hadoop design Hadoop is a low-cost and highly scalable large data place ...

The REST service can help developers to provide services to end users with a simple and unified interface. However, in the application scenario of data analysis, some mature data analysis tools (such as Tableau, Excel, etc.) require the user to provide an ODBC data source, in which case the REST service does not meet the user's need for data usage. This article provides a detailed overview of how to develop a custom ODBC driver based on the existing rest service from an implementation perspective. The article focuses on the introduction of ODBC ...

With the rise of internet web2.0 websites, the relational database has become a very hot new field, the development of the non relational database products is very rapid. 　　But the traditional relational database in dealing with web2.0 website, especially the super large-scale and high concurrent SNS type web2.0 pure dynamic website already appeared to be powerless, has exposed many insurmountable problems, for example: 1, high configured--to the database higher concurrent reads and writes the demand WEB2.0 website to be based on user personalized information to generate real-time dynamic ...

Big data has grown rapidly in all walks of life, and many organizations have been forced to look for new and creative ways to manage and control such a large amount of data, not only to manage and control data, but to analyze and tap the value to facilitate business development. Looking at big data, there have been a lot of disruptive technologies in the past few years, such as Hadoop, Mongdb, Spark, Impala, etc., and understanding these cutting-edge technologies will also help you better grasp the trend of large data development. It is true that in order to understand something, one must first understand the person concerned with the thing. So, ...

Big data has grown rapidly in all walks of life, and many organizations have been forced to look for new and creative ways to manage and control such a large amount of data, not only to manage and control data, but to analyze and tap the value to facilitate business development. Looking at big data, there have been a lot of disruptive technologies in the past few years, such as Hadoop, Mongdb, Spark, Impala, etc., and understanding these cutting-edge technologies will also help you better grasp the trend of large data development. It is true that in order to understand something, one must first understand the person concerned with the thing. So, ...

Top Ten Open Source technologies: Apache HBase: This large data management platform is built on Google's powerful bigtable management engine. As a database with open source, Java coding, and distributed multiple advantages, HBase was originally designed for the Hadoop platform, and this powerful data management tool is also used by Facebook to manage the vast data of the messaging platform. Apache Storm: A distributed real-time computing system for processing high-speed, large data streams. Storm for Apache Had ...