Actian Blog / Vector 6.3 Delivers Easier Administration, Greater Automation and Better Productivity for Data Analytics

Vector 6.3 Delivers Easier Administration, Greater Automation and Better Productivity for Data Analytics

Vector 6.3 Benefits

Did you know that Vector is one of the world’s fastest analytics databases? We’re excited to announce that a recent Enterprise Strategy Group evaluation found that Vector can outperform its competitors by up to 7.9x.

Adding to this momentum is the release of Vector 6.3 in early December. Key highlights of the release include making administration easier, enhancing engine automation, and improving programmer productivity. We’re sharing six new data analytics features and benefits that will improve your data analytics journey.

Three main groups of benefits of Vector 6.3

Automatic diagnostic log rotation ensures reliable archives

Since a log file for the Vector X100 analytics engine tends to grow very large over time, log rotation is useful to archive a current log file and open a new one. Previously a manual process, Vector 6.3 introduces automatic diagnostic log rotation based on either the log file’s maximum size or a custom time interval (e.g., every 30 days).

Query result caching – spill to disk eliminates waiting for available memory

Vector 6.3 further extends the query result cache with an option to spill cached results to disk when cache memory runs low. A job/workload does not have to wait for memory to free up before completion.

Vector disables spill to disk by default so that its overhead and workspace utilization doesn’t impact your setup after an upgrade. Once enabled, the management database can monitor spill to disk activity.

Smart min-max indexing improves memory management

A Vector database table often has several thousand columns. By default, Vector minimizes indexes and allocates large amounts of memory. Min-max reduces the number of columns inspected through new auto-tune functionality that determines scores for index and non-index columns. Based on these scores, Vector decides which columns it should add to the min-max index and which ones it should drop. As a result, this reduces memory consumption without negatively impacting query performance.

Shareable DBA User Defined Functions (UDFs) increase developer productivity

Vector supports the creation of UDFs to use in queries to extend database functionality. With release 6.3, users can now share and reuse UDFs created by DBAs through user groups and authentication, enhancing collaboration and self-service.

Exception handling for database procedures provides greater control

Improved exception handling for database procedures is now available for managing unwanted and unexpected events due to run-time errors caused by faulty design, hardware failure and code issues.

Pattern matching makes string manipulation easier

Users can now run pattern matching queries with SIMILAR TO on Vector. Vector can determine character string similarity based on character repetition, limiting character sets, character properties such as letter, hex, case, punctuation mark, and grouped characters.

Navigate Your Analytics Journey Better with Vector

Visit our website to learn more about Vector’s extensive performance optimization, features, and use cases for data analytics.

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian where she is responsible for communicating the unique value that the Avalanche Cloud Data Platform delivers, including enterprise-proven data integration, data management and data analytics. She enjoys applying her extensive knowledge in these areas to help customers find solutions that will help them achieve long-lasting success. Teresa brings a proven 20-year track record of increasing revenue and awareness for analytics, security, and cloud solutions. Prior to Actian, Teresa managed product marketing at industry-leading companies such as Cisco, McAfee, and VMware. She was also Datameer’s first VP of Marketing for big data analytics built on Hadoop, and has served as VP of Research at Giga Information Group, acquired by Forrester, providing strategic advisory services for data warehousing and analytics. Teresa holds graduate degrees in management from the MIT Sloan School of Management and software engineering from Harvard University.

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