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Actian Vector for Cloud

High-performance vectorized columnar analytic database on your choice of cloud

Fast

Fast

Double-digit performance advantages over Amazon Redshift, MS SQL Server, Snowflake and Cloudera Impala

Open

Open

Industry-standard ANSI SQL:2003 support plus integration for extensive set of data formats

Enterprise Grade

Enterprise-Grade

Updates, security, management, replication

Actian Vector in the Cloud

Actian Vector, a columnar in-memory relational database engine designed for high performance analytics, is available on both Amazon Web Services and Microsoft Azure using the bring-your-own-license (BYOL) model, for both single-node and cluster configurations.  The Actian Vector Community Edition is available as an Amazon Machine Image (AMI) for 1-click deployment through the AWS Marketplace, and also on the Microsoft Azure Marketplace.  Deploy the same fast analytics both on premise and in the cloud.

Deployment Models: Vector Enterprise Edition VectorH Enterprise Edition Vector Evaluation Edition Vector Community Edition
On-Premise License        
BYOL for Amazon AWS        
BYOL for Microsoft Azure        
AMI for AWS Marketplace  
Microsoft Azure Marketplace  

High-Performance Vectorized Columnar In-Memory Analytic Database

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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

Built for Open

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

Built for Enterprise

Built for the Enterprise

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

Dominating Performance

Vector performance advantages extend to cloud-based configurations as well, based on testing recently done by MCG, an independent global services firm specialized in information strategy and implementation services.  Vector’s performance advantage over Amazon Redshift, MS SQL Server, Snowflake, and Cloudera Impala increases as database size, query complexity, and user concurrency increases.

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Snowflake Benchmark Report

More than 20X Faster than Snowflake

Against Snowflake, MCG showed that Actian Vector is on average 6-12 times faster on a sixteen-node AWS configuration at 1-, 5-, and 10TBs, and up to 20 times on join queries.  With 20 users, Vector demonstrated significantly faster times than Snowflake, even beating Snowflake by 17X with its multi-cluster autoscaling feature turned on (at 8X the cost) to accommodate the additional users.

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Redshift Benchmark Report

Up to 14X Faster than Amazon Redshift

Based on testing using the Berkeley AMPLab big data benchmark, Vector is consistently 2-3 times faster than Redshift on a 5-node cluster, scaling from 1- to 5- to 10TBs of data.  That advantage for Vector increases with the capacity and complexity of the scan, aggregation and join workload, to nearly 14 times on the 10TB join query with 20 concurrent users.

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Almost 10X Faster than MS SQL Server

Using the same Berkeley AMPLab benchmark, MCG showed that Actian Vector is 3-6 times faster than Microsoft SQL server on a single-node AWS configuration at 500GBs, 750GBs, and 1TB.  On concurrency testing with 20 users, Vector scaled up to nearly 10 times faster than Microsoft, which couldn’t even complete some of the tests at 1TB capacity.

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Over 100X Faster than Cloudera Impala

The Berkeley AMPLab benchmark was originally created to measure Impala against other big data query engines. MCG benchmarks show Actian Vector nearly 40 times faster on average than Cloudera Impala on a 16-node AWS cluster with 1, 5, and 10TBs. Impala failed to complete the largest join query at the 10TB capacity for a single user, and most of the tests for 20 users.

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Data-driven organizations rely on analytic databases to load, store, and analyze volumes of data at high speed to derive timely insights. This benchmark addresses some fundamental business questions that any organization might encounter and ask. Actian Vector, through this industry standard set of scan, join and aggregation queries, demonstrated a significant performance advantage over Amazon Redshift and Microsoft SQL Server. These objective results were driven by the patented Actian X100 vectorized query engine which exploits the parallelism capabilities of modern server hardware.

– William McKnight, President, MCG Global Services

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Features

Revenue

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

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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

Performance Optimized

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

Hybrid

Column-Based Storage

Reduces I/O to relevant columns

Opportunity for better data compression

Built in storage indexes maximize efficiency

Compression

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

Analytics

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

Manageability

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)

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Storage Indexes

Automatic min-max indices enable block skipping on reads

Eliminates need for explicit data partitioning strategy

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Parallel Execution

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

Cloud

Flexible Deployment

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

Get Support

Community Support

Community Support

Knowledgebase

Knowledge Base

Resources

Actian Resources

Enterprise Support

Enterprise Support