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Actian – Vector Columnar Analytic Database



What is Actian Vector?

Actian Vector delivers on the promise of in-the-moment analytics with the industry’s fastest analytic database. Vector makes analytics more accessible to business users by freeing them from the common limitations of traditional data warehouses. Vector’s ability to handle continuous updates without a performance penalty makes it an Operational Data Warehouse (ODW) capable of incorporating the latest business information into your analytic decision-making. Vector achieves extreme performance with full ACID compliance on commodity hardware with the flexibility to deploy on premises, on AWS or Azure, with little or no database tuning.

Actian DataFlow provides the fastest and easiest way to extract, transform, analyze and load external data sources into Actian Vector. Learn more about DataFlow

High-Performance Vectorized Columnar Analytic Database

Built for Speed

Actian Vector is designed for speed and efficiency using column-based storage and vector processing to deliver record-breaking in-chip analytics.

Built for Open

Actian Vector enables broad access using open standards and provides extensibility through open source technologies like Spark and Hadoop.

Built for the Enterprise

Actian Vector delivers a unique combination of cutting edge innovation and mature database features that are proven in the enterprise.


Vectorized Query Execution

Exploits Single Instruction, Multiple Data (SIMD) support in x86 CPUs

Processes hundreds or thousands of elements without the overhead traditional databases have

Maximizing CPU cache for execution

Uses private CPU core and caches as execution memory – 100x faster than RAM

Delivers significantly greater throughput without limitations of in-memory approaches

Other CPU Optimizations

Supports hardware-accelerated string-based operations, benefiting selections on strings using wild card matching, aggregations on string- based values, and joins or sorts using string keys

Column-Based Storage

Reduces I/O to relevant columns

Opportunity for better data compression

Built in storage indexes maximize efficiency

Data Compression

Multiple options to maximize compression: Run Length Encoding (RLE), Patched Frame of Reference (PFOR), Delta encoding on top of PFOR, Dictionary encoding, and LZ4: for different string values

4-6x compression ratios common for real-world data

Positional Delta Trees (PDTs)

Full ACID compliance with multi-version read consistency

Changes always written persistently to a transaction log before a commit completes to ensure full recoverability

High-performance in-memory Positional Delta Trees (PDTs) handle incremental small inserts, updates and deletes without impacting query performance

Easy data migration

Move a database to a cloud or remote datacenter in one step using the integrated “clonedb” function (two steps if you include installing Vector on the remote server)

Storage Indexes

Automatic min-max indices enable block skipping on reads

Eliminates need for explicit data partitioning strategy

Parallel Execution

Flexible adaptive parallel execution algorithms to maximize concurrency while enabling load prioritization

Flexible Deployment

Available for both on-premises and cloud deployment, including both AWS Marketplace and MS Azure


Role-based security

Authentication through LDAP or Active Directory


YARN for automated Hadoop cluster resource management

Web-based management console for monitoring analytic/query processing

Spark Powered Direct Query Access

Directly access Hadoop data files stored in Parquet, ORC, or other standard formats

Realize performance benefit without converting to Vector file format first

Native Spark DataFrame Support

Direct connection to Spark functionality via DataFrames

VectorH can accelerate query performance for Spark SQL and Spark R applications

Scale-out Hadoop Performance

Linear scalability from small to large Hadoop clusters

Supported on popular Hadoop distributions from Hortonworks, Cloudera, MapR and Apache

Zero-Penalty Real-Time Data Updates

Enables full create/read/update/delete capabilities on Hadoop

Tracks changes in memory and avoids any performance penalty for updates

Extensive SQL Support

Standard ANSI SQL enabling the use of existing SQL without rewrite

Advanced analytics, including cubing, grouping, and window functions

Mature Query Optimizer

Mature and proven cost-based query planner

Optimal use of all available resources, including node, memory, cache, and CPU

MPP Architecture

Leverages Hadoop to handle thousands of users, nodes, and petabytes of data

Exploits redundancy in HDFS to provide system-wide data protection


Compress the data by at least a factor of 10 to reduce the amount of Hadoop storage

Store the data in a columnar format for faster access


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