EsgynDB
EsgynDB provides operational ease of running OLTP, ODS, BI and Analytical workloads on a single Big Data platform. EsgynDB is the only Big Data SQL solution that provides a pluggable data management framework, for disparate data sources, to handle mixed workloads (read while writing in real-time) minimizing data movement and duplication. EsgynDB reduces ETL costs by transforming data (ELT) within the database. It’s Massively Parallel Processing (MPP) architecture ensures that the strictest SLAs can be accomplished by executing queries in parallel. A mature ANSI SQL engine enables application portability over HDFS-based Data Lakes; proven to handle petabytes of data at high concurrency, and a shared nothing architecture allowing for transparent scalability with increasing cluster or data size.
History
EsgynDB has 20+ years of innovation and patented technology. HP invested more than $300 million into this technology just since 2006. The result is a database that has extensive capabilities and has been hardened across a wide variety of workloads, from OLTP, with its Tandem NonStop heritage.
Storage Model
EsgynDB database objects are stored into native Hadoop database structures. These include the following formats: • HBase that provides a Big Table, or wide-column key-value, data model • ORC or Parquet files which provides a column store data model • text files used for staging data such as comma separated value or log data • key-value sequence file
Data Model
EsgynDB is relational, supports ANSI SQL and ACID transactions. EsgynDB can query HBase structured tables and native HBase tables in the same query, but can also update them in the same ACID transaction. HBase tables can be defined as EsgynDB external tables. EsgynDB can access in the same query structured, semi-structured and unstructured data.
Query Interface
EsgynDB supports more than 150 built-in functions that include aggregate, character, date-time, mathematical, and OLAP functions. These include all ANSI functions as well as many commonly used functions from other databases, such as DECODE and TO_CHAR from Oracle. With support for BI and analytical workloads EsgynDB now sports more OLAP functions such as LEAD and LAG. It supports multiple windows in an OLAP query. It has functionality common to such workloads, such as ROLLUP and GROUPING. INTERSECT and EXCEPT operations have been added.
Query Compilation
EsgynDB’ s optimizer is based on the Cascades optimization framework. SQL Normalizer – the parsed SQL statement is passed to the normalizer which performs unconditional transformations, including subquery transformations, of the SQL into a canonical form which renders the SQL in a form that can be optimized internally. SQL Analyzer – analyzes alternative join connectivity patterns, table access paths and methods, matching partition information, etc. to be used by the optimizer’s rules. The results are passed to the plan generator for consideration in costing various plan alternatives. Table Statistics – captured equal-height histogram statistics identify data distributions for column data and correlations between columns. Sampling is used for large tables to reduce the overhead of generating the statistics. Cardinality Estimator – cardinalities, data skew, and histograms are computed for intermediate results throughout the operator tree. Cost Estimator – estimates node, I/O, and message cost for each operator while accounting for data skew at the operator level. Plan Generator – using cost estimates the optimizer considers alternative plans and chooses the plan which has the lowest cost. Where feasible the optimizer will select plans that incorporate SQL pushdown, sort elimination, and in-memory storage vs. overflow to disk. Also, it determines the optimal degree of parallelism including serial plans.
