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PART VI
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Part VI:
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SQL Today and Tomorrow
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FIGURE 26-4
Continuous query windows
Stream Database Implementations
Some of the earliest work on stream databases was conducted by university researchers from MIT, Brown, and Brandeis University, led by Dr. Michael Stonebreaker, who had pioneered earlier generations of relational database technology. In 2003, Dr. Stonebreaker and some of his research colleagues formed a company named StreamBase Systems. On the opposite coast, researchers at the University of California at Berkeley were also working on stream databases, as they had worked on earlier generations of database technology. Led by Dr. Michael Franklin, this research was based on the Postgres open-source database engine (which ironically had been initially developed by one of Dr. Stonebreaker s earlier teams). In 2005, the Berkeley research became the foundation of a venture-backed company named Truviso, focused on the stream database opportunity. In the United Kingdom, a decade of database research at Cambridge University produced Apama, a company founded in 1999. Apama focused at the applications level, developing and delivering an algorithmic trading platform that it sold to the securities industry. The underlying event-processing foundation is exposed in two other products, focused on generic Complex Event Processing (CEP) and on Business Activity Monitoring (BAM). In 2005, Progress Software acquired Apama as a complement to its ObjectStore object database. Two other earlier market players were Coral8, founded in 2003 in Mountain View, California, and Aleri, which had developed event-processing technology during its 20-year history and had added packaged applications for Wall Street trading through acquisition. The companies merged in 2009 and jettisoned the trading application to focus on combining their CEP offerings under the banner of continuous intelligence. All of the market participants focus primarily on Wall Street securities trading applications, because that market segment has the clearest requirements for the capabilities that streaming databases uniquely provide, and also has the largest opportunities for
26:
Specialty Databases
Development environment
Dashboard
Data feed
Input adapter Input adapter
Eventprocessing engine
Output adapter
Data feed Data feed
DBMS link
Enterprise database
FIGURE 26-5
Elements of a streaming database
CEP applications to significantly increase revenue. Federal government applications (especially in the intelligence community for analysis of gathered intelligence streams) also figure prominently for several of the vendors. All of the vendors cite some successes in web-based businesses or in telecom as evidence of the potential for broader market appeal, but the deployments outside the Wall Street and government sectors have been limited to date.
Stream Database Components
The market for stream databases has matured to the point where all of the vendors offer a similar suite of facilities. The major elements, illustrated in Figure 26-5, usually include most of the following: An event-processing engine accepts incoming messages from a data feed or network, and carries out continuous queries against the data. The engine may also include the ability to combine data from the stream with data from its own static tables, or from static data managed by a traditional enterprise DBMS. A suite of input adapters accepts incoming messages from common data feeds and passes them to the event-processing engine in a standard format. The adapters typically support the Java Messaging Service (JMS) API, Tibco s popular Rendezvous messaging service, various feeds from the financial markets, and other enterprise messaging systems. A suite of output adapters accepts messages passed through by the eventprocessing engine or generated by it, and translates them for transmission on common messaging systems, such as JMS-compatible systems or Rendezvous.
PART VI
Part VI:
SQL Today and Tomorrow
Enterprise DBMS links allow the event-processing engine to merge data from the data feed with data retrieved from traditional enterprise databases. All of the major products support database access via JDBC, and some provide support for proprietary enterprise database APIs. A development environment allows programmers to build continuous queries and to test and deploy them as production systems. Some vendors offer graphical development environments to specify data filtering, merging, joining, and grouping. Others provide support for a text-based SQL-based language that is used to specify the queries. The development environment may also support a modeling language to define the formats of the various data streams and how the data is to be manipulated by the engine. A dashboard or portal provides a graphical view of the query results, visually displaying averages, totals, exceptions, and the like in real time as the eventprocessing engine carries out its work on the streaming data.
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