Checkpoints

Not Supported

Compression

Dictionary Encoding

ActivePivot uses both dictionary encoding and Java primitives for compression.

Dictionary Compression: When there is a small number of distinct values for an attribute, the attributes can be encoded into an integer from 1 to N where N denotes the total number of distinct values by using a dictionary that stores the distinct values.

Java Primitives: Instead of storing integers as Java Integer objects, ActivePivot stores these as primitives. The number of bits stored for each number is based on the range of possible values. For example, if the values in a column are given to be between 0 and 256 then the system would only store 8 bits per number. This form of compression can be combined with dictionary compression

Concurrency Control

Multi-version Concurrency Control (MVCC)

The system allows for multiple versions of a database using delta storage to keep track of different versions. For example, there can be one version that continuously gathers incoming data, and another version from an earlier point in time for analysis.

Data Model

Relational

Both relational tables and multidimensional OLAP cubes are used for organizing the data.

Foreign Keys

Supported

Hardware Acceleration

Custom

ActivePivot provides support for multi-core processing and non-uniform memory access architectures. SIMD instructions can be used in accelerating operations such as column scans and aggregations.

Indexes

BitMap

ActivePivot uses bitmap indexing for multidimensional cubes and supports primary and secondary indexes for relational tables.

Isolation Levels

Snapshot Isolation

Logging

Not Supported

Parallel Execution

Intra-Operator (Horizontal)

When a server receives a query, it also sends the query out to the other servers. Once it receives a response from all the servers it aggregates all of the information and returns the result.

Query Compilation

JIT Compilation

The Java JIT compiler is responsible for handling the compilation of Multidimensional Expression (MDX) queries. MDX queries are similar in syntax to SQL queries, but they serve a different purpose. While SQL queries operate on tables which are two dimensional, MDX queries operate on OLAP cubes, which can support many dimensions. Some other operations are compiled directly into Java byte code.

Query Interface

Custom API

ActivePivot supports queries through Multidimensional Expressions (MDX) either through ActiveUI or any another MDX-compliant interface.

Storage Architecture

In-Memory

The system takes advantage of non-uniform memory access (NUMA) by partitioning the tables based upon the number of CPU cores available on servers.

Storage Model

Decomposition Storage Model (Columnar)

Data is stored in columns in memory, DRAM or flash, with no additional row-based storage.

System Architecture

Shared-Nothing

All servers act independently and do not have access to the information contained on other servers. Data can be distributed between servers based on the values of a given attribute. For example, if one of the attributes is city then the data can be distributed based on geographic regions.

Views

Materialized Views

The system supports materialized views for both OLAP cubes and continuous queries. Views can be materialized on a subset of dimensions for the OLAP cube. For continuous queries, the materialized view of the result is updated alongside changes to the underlying data.