Elastic Scalability

eXtremeDB utilizes distributed query processing to ensure high scalability and low latency.

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eXtremeDB offers ultra-fast, elastically scalable data management.

With this feature, databases are partitioned (“sharded”), with each partition/shard managed by an instance of the DBMS server. Shards are typically distributed on a storage array (which may be a SAN) – with each server keeping a CPU core busy – or distributed across different physical servers with their own storage systems.

Each shard can have one or more backup (replica) copies, which in addition to delivering high availability via failover, can also share the query processing load. Distributed query processing across multiple servers, CPUs and/or CPU cores accelerates performance.  The difference can be dramatic in some cases, with parallel execution of database operations and by harnessing the combined processing power, memory and I/O bandwidth of many nodes rather than just one.

eXtremeDB offers ultra scalable data management

Sharding, with distributed query processing, leverages the processing power, memory and bandwidth of multiple hardware nodes. Each database shard can have one or more backup (replica) copies.

The developer can specify the storage (in-memory or persistent) for each table, which is ideal for handling real-time quote and historical data within a single database architecture. Or, all tables can be in memory, or all persistent.

 

Review the audited benchmarks

Distributed query processing played a key role in eXtremeDB’s record-setting STAC-M3 benchmark implementations. For example, eXtremeDB on an IBM POWER8 S824L Linux server achieved best ever overall processing time and lowest standard deviation of results (low jitter) with processing spread across 72 shards. Learn more about eXtremeDB scalable data management for time series data.  Evaluate a free trial of the eXtremeDB scalable SQL database.

 

Flexible data layout

eXtremeDB for HPC implements columnar data layout for fields of type ‘sequence’. Sequences can be combined to form a time series, ideal for working with tick streams, historical quotes and other sequential data. This technology supports database designs that combine row-based and column-based layouts, in order to best leverage the CPU cache speed.  Learn more.

Scalable distributed database contrast and compare

eXtremeDB offers different distributed database options to address different objectives. This table summarizes primary purpose and characteristics of each distributed database option (some of which may be combined, e.g. Sharding, Cluster and High Availability).  Learn more.

Learn more about scalable time series data management.

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eXtremeDB combines scalable data management and speed.