TPC-H 10G Results
All 22 queries of TPC-H are measured against Greenplum DB and Deepgreen DB. Q1 and Q5 are specifically graphed below for comparisons.
Q1: Scan and aggregate fact table
Raw result: Deepgreen DB vs Greenplum DB using Heap Tables
Q1 is a typical aggregate query running against the fact table.
Q5 is an aggregate over a 6-way hashjoin that joins the fact table lineitem table against the orders and supplier tables, and subsequently against other dimension tables.
Features Deepgreen DB
Executor tuned for x86
AI & Machine Learning
Non-stop & incremental
lz4, zstd, zlib, quicklz
Load & Connectivity
Xdrive, gpfdist, gpload
In-memory Data Grid
100% Compatible with Greenplum DB
Deepgreen DB is derived from the open source Greenplum DB project. It maintains 100% compatibility with Greenplum DB. From SQL and stored procedures syntax, to storage formats on disk, to operation utilities such as gpstart or gpfdist, Deepgreen DB ensures full compatibility to minimize effort in redeployment. In particular:
No need to reload data.
No changes to SQL code (both DML and DDL).
No changes to stored procedure code.
No changes to user-defined function code.
No changes to connectivity and
Authentication protocols such as odbc and jdbc.
No changes to operational scripts such as
Bash backup scripts and cron jobs.
Deepgreen DB Application Area
For most OLAP workload that is CPU-bound, Deepgreen DB runs up to 3X faster than Greenplum DB on average.
Using Xdrive, Deepgreen DB can read/write to/from many external data external sources in a distributed and efficient manner.
Using the Transducer, Python and Go code fragments can be directly embedded into SQL to group and push data to TensorFlow for machine learning.