Welcome to Alenka - GPU database engine
What is this?
This is a GPU based database engine written to use vector based processing and high bandwidth of modern GPUs
Requirements
- CUDA (nvcc) + Nvidia GPU
- bison
- flex
- Modern GPU Library (included as submodule)
How to build?
git clone --recursive https://github.com/antonmks/Alenka.git
cd Alenka
make
Features :
-
Vector-based processing
CUDA programming model allows a single operation to be applied to an entire set of data at once. -
Smart compression
Ultra fast compression and decompression on GPU.
Database operations on compressed data. -
Column-based storage
Minimizes disk I/O by only accessing the relevant data. -
Data skipping
Better performance without indexes. -
Fast Loading
Gpu based CSV parser loads the data into database at very high speed.
How to use it ?
Create your data files :
Run scripts load_orders.sql, load_lineitem.sql and load_customer.sql to create your database files.
Run your queries from a command prompt or use Alenka JDBC driver from Technica Corporation
Step 1 - Filter data
OFI := FILTER orders BY o_orderdate < 19950315;
CF := FILTER customers BY c_mktsegment == "BUILDING";
LF := FILTER lineitem BY shipdate > 19950315;
Step 2 - Join data
OLC := SELECT o_orderkey AS o_orderkey, o_orderdate AS o_orderdate,
o_shippriority AS o_shippriority, price AS price, discount AS discount
FROM LF JOIN OFI ON orderkey = o_orderkey
JOIN CF ON o_custkey = c_custkey;
Step 3 - Group data
F := SELECT o_orderkey AS o_orderkey1, o_orderdate AS orderdate1,
o_shippriority AS priority, SUM(price*(1-discount)) AS sum_revenue, COUNT(o_orderkey) AS cnt
FROM OLC GROUP BY o_orderkey, o_orderdate, o_shippriority;
Step 4 - Order data
RES := ORDER F BY sum_revenue DESC, orderdate1 ASC;
Step 5 - Save the results
STORE RES INTO 'results.txt' USING ('|') LIMIT 10;
####Alenka is licensed under Apache 2.0 license.