Pivotal SQLFire

Take Advantage of More Opportunities—At the Right Time, In the Right Context

Your company can gain sustainable competitive advantage—increased revenue, improved safety, reduced fraud—by acting in the right moment. All it takes is understanding your big data—in real time.

SQL is an expressive powerful querying language that allows your users to get insights into their data. With Pivotal SQLFire™, a real-time distributed data store that solves the hard problems of distributed systems, your company can tap into data from multiple sources to correlate and create feedback loops that inform decision making. Pivotal SQLFire leverages more than 10 years of GemFire research and development, as well as experience in enterprise-class customer environments. Through a familiar SQL interface, SQLFire accommodates ever-growing data sets and users with linear scalability, continuous uptime and predictable performance. SQLFire is built for data consistency and cloud scalability.

PIVOTAL SQLFire Features

Solve the Data Challenges Presented by Modern, Distributed Web-Oriented Applications with SQL

SQLFire is the premier in-memory data platform delivering speed and dynamic scalability, together with the reliability and data management capabilities of a traditional database. Fast and flexible, SQLFire enables you to process massive quantities of data and scale within the cloud to suit the needs of users, while providing tools to simplify monitoring and management. The SQLFire solution features

  • Data Consistency with Cloud Scalability – SQLFire solves the problem of moving data into the cloud by implementing a shared-nothing architecture. The only shared resource in the cluster is the network. Adding nodes to the cluster adds capacity for more users and data. The application tier does not need to be aware of how data is being made consistent, how big the cluster is, how many copies of data are available, or what the current cluster membership is. Typically an issue of complex maintenance, scripts and custom code, this logic is all built into SQLFire. The solution can be configured for a high level of data availability by using redundancy settings. The logic used to update data ensures that all copies of the data in the cluster are consistent at any given point. And data can be sent to remote clusters via the WAN Gateway in case of catastrophic failures. SQLFire can also be used as a reliable Operational Data Store, leveraging archival systems for OLAP and offloading of data when it is no longer relevant.
  • Extreme Performance and Continuous Uptime with Predictable Performance – SQLFire provides for continuous uptime with built in high availability and disaster recovery. Multiple failure detection models detect and react to failures quickly, ensuring that the cluster is always available, and that the data set is always complete. During hardware refreshes and major SQLFire upgrades, SQLFire’s cloud elasticity allows you to add nodes to the cluster without impacting the clients. During data model changes, SQLFire uses PDX Serialization to enable different nodes and clients in a cluster to have different data model versions running at the same time. This means that new data models can be deployed with zero impact to client applications. During server-logic code changes, SQLFire provides hot redeployment of any user logic classes via our management tool, gfsh.
  • Data Aware Parallel Function Execution – By associating a function with a data set, SQLFire will route function calls to the relevant nodes on the cluster without interference from the caller. For large data sets, this means dramatically reducing the total time to run complex calculations over traditional approaches. As the SQLFire cluster expands and contracts, the calling application does not need to be aware of the cluster membership or where the data resides. Application developers can use a familiar HashMap interface to touch the data directly, or an easy-to-use function API to interact with the data set. If a failure occurs in the cluster during function execution, the function can be configured to be aware of this and restart the execution.
  • Data Stream with Enterprise Data Store Correlation – SQLFire can receive data from any application that is able to call a C++, C#, Java, or REST interface. Conversely, SQLFire can call into anything that is available via a Java API, such as JDBC or Web Services. Along with a powerful query interface, data is readily accessible. This flexible API, along with the ability to ingest data from external sources and send out event notifications to other systems, gives you the ability to correlate data from multiple sources into real time information that is important for your enterprise.

PIVOTAL SQLFire Technology

What Is SQLFire and How Is It Powered by GemFire?

SQLFire leverages the decade of research and development that went into making GemFire the data grid of choice for some of the biggest enterprises in the world. Because of its GemFire legacy, SQLFire supports highly optimized in-memory data management, split-brain detection, group membership management, highly optimized metadata management, extreme data volume support in-memory and on disk, high availability through a variety of techniques, WAN replication capabilities, in-memory stored procedure support, and a scalable management and monitoring framework.

Relational Technology Based on Apache Derby

SQLFire leverages Apache Derby to provide a familiar SQL interface to applications while leveraging the in-memory technology, the elasticity and the scaling characteristics of GemFire. The combination of these two very mature code bases provides a product that delivers stellar performance, as well as the broad SQL support needed for OLTP applications.

Referential Integrity

One of the important characteristics of relational databases is their ability to support referential integrity ensuring that foreign key relationships are honored in a transactional system. SQLFire supports referential integrity in the system and allows your users to configure foreign key relationships across tables and enforces these at run time. In this area, most NoSQL products cannot compare with SQLFire and traditional databases.

Standards-Compliant Powerful Querying Engine

SQLFire provides a powerful querying engine that is optimized for a distributed system. It includes a distributed cost-based optimizer that ensures the generation of optimal query plans to satisfy queries accessing data across the system. Most common queries and Data Manipulation Language (DML) statements are based on ANSI SQL-92, so experienced database application developers can use their knowledge of SQL when working with SQLFire. SQLFire is implemented entirely in Java, and it can be embedded directly within a Java application. The use of JDBC, ADO.NET and ODBC (coming soon) drivers ensures that application code accessing the database can use familiar programming constructs without being subject to the scaling limitations of traditional databases.

Data-Dependent and Data-Aware Java Stored Procedures

SQLFire enables applications to run both data-aware and data-independent stored procedures on the cluster. These stored procedures run in parallel and provide results back to the sender, allowing for both synchronous, asynchronous and partially synchronous behavior execution in the data grid. These stored procedures enjoy extremely high throughput since they execute on data that is typically in-memory.


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