NOTICE: This project have moved to Databend

---

VectorSQL is a free analytics DBMS for IoT & Big Data, compatible with ClickHouse.

Features

• High Performance
• High Scalability
• High Reliability

Server

$git clone https://github.com/vectorengine/vectorsql
$cd vectorsql
$make build
$./bin/vectorsql-server -c conf/vectorsql-default.toml
	
 2020/01/27 19:02:39.245654    	 [DEBUG] 	Database->Attach Table:system.tables, engine:SYSTEM_TABLES <attachTable@database_system.go:116>
 2020/01/27 19:02:39.245670    	 [DEBUG] 	Database->Attach Table:system.databases, engine:SYSTEM_DATABASES <attachTable@database_system.go:116>
 2020/01/27 19:02:39.245680    	 [INFO] 	Database->Load Database:system <loadSystemDatabases@databases.go:110>
 2020/01/27 19:02:39.245794    	 [INFO] 	Listening for connections with native protocol (tcp)::9000 <Start@server.go:33>
 2020/01/27 19:02:39.245806    	 [INFO] 	Servers start... <main@server.go:62>

Client

• clickhouse-client

$clickhouse-client --compression=0
VectorSQL :) SELECT SUM(IF(status!=200, 1, 0)) AS errors, SUM(IF(status=200, 1, 0)) as success, (errors/COUNT(server)) AS error_rate,(success/COUNT(server)) as success_rate, (SUM(response_time)/COUNT(server)) AS load_avg, MIN(response_time), MAX(response_time), path, server FROM logmock(rows->15) GROUP BY server, path HAVING errors>0 ORDER BY server ASC, load_avg DESC;

SELECT 
    SUM(IF(status != 200, 1, 0)) AS errors, 
    SUM(IF(status = 200, 1, 0)) AS success, 
    errors / COUNT(server) AS error_rate, 
    success / COUNT(server) AS success_rate, 
    SUM(response_time) / COUNT(server) AS load_avg, 
    MIN(response_time), 
    MAX(response_time), 
    path, 
    server
FROM logmock(rows -> 15)
GROUP BY 
    server, 
    path
HAVING errors > 0
ORDER BY 
    server ASC, 
    load_avg DESC

┌─errors─┬─success─┬─error_rate─┬─success_rate─┬─load_avg─┬─MIN(response_time)─┬─MAX(response_time)─┬─path───┬─server──────┐
│      2 │       1 │     0.6667 │       0.3333 │       12 │                 10 │                 13 │ /login │ 192.168.0.1 │
│      1 │       5 │     0.1667 │       0.8333 │  11.1667 │                 10 │                 12 │ /index │ 192.168.0.1 │
│      1 │       3 │       0.25 │         0.75 │    11.25 │                 10 │                 14 │ /index │ 192.168.0.2 │
│      1 │       1 │        0.5 │          0.5 │       11 │                 10 │                 12 │ /login │ 192.168.0.2 │
└────────┴─────────┴────────────┴──────────────┴──────────┴────────────────────┴────────────────────┴────────┴─────────────┘
↓ Progress: 0.00 rows, 0.00 B (0.00 rows/s., 0.00 B/s.) 
4 rows in set. Elapsed: 0.005 sec. 

• http-client

curl -XPOST http://127.0.0.1:8123 -d "SELECT SUM(IF(status!=200, 1, 0)) AS errors, SUM(IF(status=200, 1, 0)) as success, (errors/COUNT(server)) AS error_rate,(success/COUNT(server)) as success_rate, (SUM(response_time)/COUNT(server)) AS load_avg, MIN(response_time), MAX(response_time), path, server FROM logmock(rows->15) GROUP BY server, path HAVING errors>0 ORDER BY server ASC, load_avg DESC"
2	1	0.6667	0.3333	12.0000	10	13	/login	192.168.0.1
1	5	0.1667	0.8333	11.1667	10	12	/index	192.168.0.1
1	3	0.2500	0.7500	11.2500	10	14	/index	192.168.0.2
1	1	0.5000	0.5000	11.0000	10	12	/login	192.168.0.2

Query Language Features

Query language	Current version	Future versions	Example
Scans by Value	+	+	SELECT a,b
Scans by Expression	+	+	SELECT IF(a>2,a,b),SUM(a)
Filter by Value	+	+	WHERE a>10
Filter by Expression	+	+	WHERE a>(b+10)
Group-Aggregate by Value	+	+	GROUP BY a
Group-Aggregate by Expression	+	+	GROUP BY (a+1)
Group-Having by Value	+	+	HAVING count_a>2
Group-Having by Expression	+	+	HAVING (count_a+1)>2
Order by Value	+	+	ORDER BY a desc
Order by Expression	+	+	ORDER BY (a+b)
Window Functions	-	+
Common Table Expressions	-	+
Join	-	+

Performance

• Dataset: 10,000,000 (10 Million)
• Hardware: 16vCPUx16G KVM Cloud Instance
• Benchmark

Query	Cost(second)
SELECT COUNT(id) FROM testdata	0.269s
SELECT COUNT(id) FROM testdata WHERE id!=0	0.438s
SELECT SUM(data1) FROM testdata	0.287s
SELECT SUM(data1) AS sum, COUNT(data1) AS count, sum/count AS avg FROM testdata	1.814s
SELECT MAX(id), MIN(id) FROM testdata	0.473s
SELECT COUNT(data1) AS count, data1 FROM testdata GROUP BY data1 ORDER BY count DESC LIMIT 10	0.728s
SELECT email FROM testdata WHERE email like '%20@example.com%' LIMIT 1	0.076s
SELECT COUNT(email) FROM testdata WHERE email like '%20@example.com%'	1.470s
SELECT data1 AS x, x - 1, x - 2, x - 3, count(data1) AS c FROM testdata GROUP BY x, x - 1, x - 2, x - 3 ORDER BY c DESC LIMIT 10	2.396s

Metrics

http://localhost:8080/debug/metrics