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PolarDB:ClickBench tests

Last Updated:Jan 21, 2025

ClickBench is a benchmarking tool developed by ClickHouse, Inc to evaluate database performance in large-scale analytical scenarios. ClickBench includes a dataset with 43 SQL queries specifically created for performance testing. This topic describes how to use ClickBench to test the performance of read-only columnar instances.

Test design

Test data

Test table: 105 columns, including 19 INTEGER columns, 6 BIGINT columns, 48 SMALLINT columns, 26 TEXT columns, 1 VARCHAR column, 1 TIMESTAMP column, and 1 DATE column.

Data amount: approximately 100 million rows.

Dataset size: 70 GB.

Data examples:

9110818468285196899	0		1	2013-07-14 20:38:47	2013-07-15	17	-1216690514	839	-2461439046089301801	0	0	0			0	0	0	0	0	0	0	0	0	0		0	0	0	�O	0	0	0	0			3793327	4	0		0	0	0	0	-1	1971-01-01 14:16:06	0	0	0	0		0	0	0	0	0		0	0	0	0	0	0	5	2013-07-15 10:47:34	0	0	0	0	0	-1001831330	-1	-1	-1	�	�\f			0	0	0	0	0	0	0	0		0		NH	0											0	-296158784638538920	-8417682003818480435	0
8156744413230856864	0		1	2013-07-15 18:33:50	2013-07-15	17	-1216690514	839	-2461439046089301801	0	0	0			0	0	0	0	0	0	0	0	0	0		0	0	0	�O	0	0	0	0			3793327	4	0		0	0	0	0	-1	1971-01-01 14:16:06	0	0	0	0		0	0	0	0	0		0	0	0	0	0	0	5	2013-07-15 08:37:59	0	0	0	0	0	-1001831330	-1	-1	-1	�	�\f			0	0	0	0	0	0	0	0		0		NH	0											0	-296158784638538920	-8417682003818480435	0
5581899925183342605	1	@дневники, работа и женщин поступивая ул, попохорошем качество дал	1	2013-07-15 15:45:23	2013-07-15	38	2050260421	2	-9214751021948998350	0	44	5	https://produkty%2Fkategory_id=0&last_auto_id=&autodoc.ru/proskategory/sell/reside.travel.ru/recipe/viewtopic,375;sa=shop.ru/san	https://go.mail/folder-1/online/ru-en/#lingvo/#1О 50000&price_ashka/rav4/page=/check.xml	0	14550	952	15014	519	1917	879	37	15	13	800	0	0	31	D�	1	1	0	0			209623	3	2	авомосква веб каменисный	0	0	745	438	135	2013-07-15 10:14:30	4	1	31337	0	windows-1251;charset	1601	0	0	0	8570394295480778849		178995092	0	0	0	0	0	5	2013-07-15 15:10:45	31	1	2	70	17	1437531235	-1	-1	-1	S0	�\f			0	0	0	3	1300	460	284	0		0		NH	0											0	-823144271007412007	-5847714421347370287	0
8407760668305829074	1	@дневники, работа и женщин поступивая ул, попохорошем качество дал	1	2013-07-15 15:45:24	2013-07-15	38	2050260421	2	-9214751021948998350	0	44	5	https://produkty%2Fkategory_id=0&last_auto_id=&autodoc.ru/proskategory/sell/reside.travel.ru/recipe/viewtopic,375;sa=shop.ru/san	https://go.mail/folder-1/online/ru-en/#lingvo/#1О 50000&price_ashka/rav4/page=/check.xml	1	14550	952	15014	519	1917	879	37	15	13	800	0	0	31	D�	1	1	0	0			209623	3	2	авомосква веб каменисный	0	1	745	438	135	2013-07-15 10:14:30	4	1	31337	0	windows-1251;charset	1601	0	0	0	8570394295480778849		178995092	0	0	0	0	0	5	2013-07-15 15:10:46	31	1	2	70	17	1437531235	-1	-1	-1	S0	�\f			0	0	0	0	0	0	0	0		0		NH	0											0	-823144271007412007	-5847714421347370287	0

Test SQL examples:

SELECT COUNT(*) FROM hits WHERE AdvEngineID <> 0;
SELECT AdvEngineID, COUNT(*) FROM hits WHERE AdvEngineID <> 0 GROUP BY AdvEngineID ORDER BY COUNT(*) DESC;
SELECT RegionID, COUNT(DISTINCT UserID) AS u FROM hits GROUP BY RegionID ORDER BY u DESC LIMIT 10;

Instance specifications

Note

Specification of the read-only columnar instance

Number of compute nodes in the read-only columnar instance

4 cores, 32 GB memory

2

8 cores, 32 GB memory

2

8 cores, 32 GB memory

4

16 cores, 64 GB memory

2

16 cores, 64 GB memory

4

Specifications of the ECS instance

ecs.g8i.16xlarge (64 vCPUs, 256 GB memory, 200 GB disk space), JDK 11 installed

Test method

Preparations

  1. Execute the following commands on the ECS instance to download and decompress the dataset package:

    wget https://datasets.clickhouse.com/hits_compatible/hits.tsv.gz
    gunzip hits.tsv.gz
  2. Execute the following SQL statements on the primary PolarDB-X instance to create a test database and table:

    CREATE DATABASE clickbench MODE = 'auto';
    CREATE TABLE hits
    (
        WatchID BIGINT NOT NULL,
        JavaEnable SMALLINT NOT NULL,
        Title TEXT NOT NULL,
        GoodEvent SMALLINT NOT NULL,
        EventTime TIMESTAMP NOT NULL,
        EventDate Date NOT NULL,
        CounterID INTEGER NOT NULL,
        ClientIP INTEGER NOT NULL,
        RegionID INTEGER NOT NULL,
        UserID BIGINT NOT NULL,
        CounterClass SMALLINT NOT NULL,
        OS SMALLINT NOT NULL,
        UserAgent SMALLINT NOT NULL,
        URL TEXT NOT NULL,
        Referer TEXT NOT NULL,
        IsRefresh SMALLINT NOT NULL,
        RefererCategoryID SMALLINT NOT NULL,
        RefererRegionID INTEGER NOT NULL,
        URLCategoryID SMALLINT NOT NULL,
        URLRegionID INTEGER NOT NULL,
        ResolutionWidth SMALLINT NOT NULL,
        ResolutionHeight SMALLINT NOT NULL,
        ResolutionDepth SMALLINT NOT NULL,
        FlashMajor SMALLINT NOT NULL,
        FlashMinor SMALLINT NOT NULL,
        FlashMinor2 TEXT NOT NULL,
        NetMajor SMALLINT NOT NULL,
        NetMinor SMALLINT NOT NULL,
        UserAgentMajor SMALLINT NOT NULL,
        UserAgentMinor VARCHAR(255) NOT NULL,
        CookieEnable SMALLINT NOT NULL,
        JavascriptEnable SMALLINT NOT NULL,
        IsMobile SMALLINT NOT NULL,
        MobilePhone SMALLINT NOT NULL,
        MobilePhoneModel TEXT NOT NULL,
        Params TEXT NOT NULL,
        IPNetworkID INTEGER NOT NULL,
        TraficSourceID SMALLINT NOT NULL,
        SearchEngineID SMALLINT NOT NULL,
        SearchPhrase TEXT NOT NULL,
        AdvEngineID SMALLINT NOT NULL,
        IsArtifical SMALLINT NOT NULL,
        WindowClientWidth SMALLINT NOT NULL,
        WindowClientHeight SMALLINT NOT NULL,
        ClientTimeZone SMALLINT NOT NULL,
        ClientEventTime TIMESTAMP NOT NULL,
        SilverlightVersion1 SMALLINT NOT NULL,
        SilverlightVersion2 SMALLINT NOT NULL,
        SilverlightVersion3 INTEGER NOT NULL,
        SilverlightVersion4 SMALLINT NOT NULL,
        PageCharset TEXT NOT NULL,
        CodeVersion INTEGER NOT NULL,
        IsLink SMALLINT NOT NULL,
        IsDownload SMALLINT NOT NULL,
        IsNotBounce SMALLINT NOT NULL,
        FUniqID BIGINT NOT NULL,
        OriginalURL TEXT NOT NULL,
        HID INTEGER NOT NULL,
        IsOldCounter SMALLINT NOT NULL,
        IsEvent SMALLINT NOT NULL,
        IsParameter SMALLINT NOT NULL,
        DontCountHits SMALLINT NOT NULL,
        WithHash SMALLINT NOT NULL,
        HitColor CHAR NOT NULL,
        LocalEventTime TIMESTAMP NOT NULL,
        Age SMALLINT NOT NULL,
        Sex SMALLINT NOT NULL,
        Income SMALLINT NOT NULL,
        Interests SMALLINT NOT NULL,
        Robotness SMALLINT NOT NULL,
        RemoteIP INTEGER NOT NULL,
        WindowName INTEGER NOT NULL,
        OpenerName INTEGER NOT NULL,
        HistoryLength SMALLINT NOT NULL,
        BrowserLanguage TEXT NOT NULL,
        BrowserCountry TEXT NOT NULL,
        SocialNetwork TEXT NOT NULL,
        SocialAction TEXT NOT NULL,
        HTTPError SMALLINT NOT NULL,
        SendTiming INTEGER NOT NULL,
        DNSTiming INTEGER NOT NULL,
        ConnectTiming INTEGER NOT NULL,
        ResponseStartTiming INTEGER NOT NULL,
        ResponseEndTiming INTEGER NOT NULL,
        FetchTiming INTEGER NOT NULL,
        SocialSourceNetworkID SMALLINT NOT NULL,
        SocialSourcePage TEXT NOT NULL,
        ParamPrice BIGINT NOT NULL,
        ParamOrderID TEXT NOT NULL,
        ParamCurrency TEXT NOT NULL,
        ParamCurrencyID SMALLINT NOT NULL,
        OpenstatServiceName TEXT NOT NULL,
        OpenstatCampaignID TEXT NOT NULL,
        OpenstatAdID TEXT NOT NULL,
        OpenstatSourceID TEXT NOT NULL,
        UTMSource TEXT NOT NULL,
        UTMMedium TEXT NOT NULL,
        UTMCampaign TEXT NOT NULL,
        UTMContent TEXT NOT NULL,
        UTMTerm TEXT NOT NULL,
        FromTag TEXT NOT NULL,
        HasGCLID SMALLINT NOT NULL,
        RefererHash BIGINT NOT NULL,
        URLHash BIGINT NOT NULL,
        CLID INTEGER NOT NULL,
        PRIMARY KEY (CounterID, EventDate, UserID, EventTime, WatchID)
    ) partition by key(UserID) partitions 24;
  3. Import data.

    Save the following command to a file named load.sh on the ECS instance and run the script by executing the sh load.sh command to import data:

    java -Xmn4g -Xmx6g -jar batch-tool.jar -h127.0.0.1 -P3306 -uroot -pPassword -D clickbench -o import -t hits -s "       " -pro 1 -con 16 -minConn 8 -maxConn 16 -batchSize 100 -f hits.tsv -quote AUTO 2>&1 >> hits.log
    Note

    For the installation and usage instructions for batch-tool.jar, see Use Batch Tool to export and import data.

    Parameters:

    Parameter

    Description

    -h

    The database connection address.

    -P

    The port number for the database connection.

    -u

    The username for the database connection.

    -p

    The password for the database connection.

    -D

    The database name, which is clickbench in this example.

    -t

    The table name, which is hits in this example.

    -s

    The data separator in the hits.tsv file after the dataset is decompressed, which is a tab character in this example.

    -f

    The name of the data file, which is hits.tsv in this example.

    -Xmn

    The size of the JVM young generation, which can be adjusted based on the ECS configuration.

    -Xmx

    The maximum heap size for the JVM, which can be adjusted based on the ECS configuration.

    Note

    The preceding parameters can be adjusted based on your business requirements.

  4. After data is imported, execute the following SQL statement in the clickbench database to create a clustered columnar index (CCI) for the hits table:

    CREATE CLUSTERED COLUMNAR INDEX cci_hits ON hits(EventDate) PARTITION BY HASH(`UserID`) PARTITIONS 64;
    Note

    For more information, see CCI.

Database parameter tuning

SET GLOBAL RECORD_SQL = false;
SET GLOBAL MPP_METRIC_LEVEL = 0;
SET GLOBAL ENABLE_CPU_PROFILE = false;
SET GLOBAL ENABLE_BACKGROUND_STATISTIC_COLLECTION=false;
SET GLOBAL ENABLE_STATISTIC_FEEDBACK=false;
SET GLOBAL ENABLE_MPP_SERIALIZED_CHUNK_COMPRESSION = true;
SET GLOBAL ENABLE_OSS_COMPATIBLE = false;
SET GLOBAL MPP_TASK_LOCAL_MAX_BUFFER_SIZE = 32000000000;
SET GLOBAL MPP_OUTPUT_MAX_BUFFER_SIZE = 32000000000;
SET GLOBAL MPP_EXCHANGE_MAX_RESPONSE_SIZE = 32000000000;
SET GLOBAL ENABLE_STREAM_PARTIAL_AGG = true;
SET GLOBAL ENABLE_TRANSPARENT_PARTIAL_AGG = true;
SET GLOBAL ENABLE_SIMPLIFY_GROUP_BY_RULE = true;
Note

Disable logging, CPU profiling, and automatic collection of statistics for the database.

Execute the test script

  1. Save the following code to a file named click.sh on the ECS instance and specify the -h, -P, -u, and -D parameters in click.sh based on your business requirements:

    sql_queries=("SELECT COUNT(*) FROM hits;"
        "SELECT COUNT(*) FROM hits WHERE AdvEngineID <> 0;"
        "SELECT SUM(AdvEngineID), COUNT(*), AVG(ResolutionWidth) FROM hits;"
        "SELECT AVG(UserID) FROM hits;"
        "SELECT COUNT(DISTINCT UserID) FROM hits;"
        "SELECT COUNT(DISTINCT SearchPhrase) FROM hits;"
        "SELECT MIN(EventDate), MAX(EventDate) FROM hits;"
        "SELECT AdvEngineID, COUNT(*) FROM hits WHERE AdvEngineID <> 0 GROUP BY AdvEngineID ORDER BY COUNT(*) DESC;"
        "SELECT RegionID, COUNT(DISTINCT UserID) AS u FROM hits GROUP BY RegionID ORDER BY u DESC LIMIT 10;"
        "SELECT RegionID, SUM(AdvEngineID), COUNT(*) AS c, AVG(ResolutionWidth), COUNT(DISTINCT UserID) FROM hits GROUP BY RegionID ORDER BY c DESC LIMIT 10;"
        "SELECT MobilePhoneModel, COUNT(DISTINCT UserID) AS u FROM hits WHERE MobilePhoneModel <> '' GROUP BY MobilePhoneModel ORDER BY u DESC LIMIT 10;"
        "SELECT MobilePhone, MobilePhoneModel, COUNT(DISTINCT UserID) AS u FROM hits WHERE MobilePhoneModel <> '' GROUP BY MobilePhone, MobilePhoneModel ORDER BY u DESC LIMIT 10;"
        "SELECT SearchPhrase, COUNT(*) AS c FROM hits WHERE SearchPhrase <> '' GROUP BY SearchPhrase ORDER BY c DESC LIMIT 10;"
        "SELECT SearchPhrase, COUNT(DISTINCT UserID) AS u FROM hits WHERE SearchPhrase <> '' GROUP BY SearchPhrase ORDER BY u DESC LIMIT 10;"
        "SELECT SearchEngineID, SearchPhrase, COUNT(*) AS c FROM hits WHERE SearchPhrase <> '' GROUP BY SearchEngineID, SearchPhrase ORDER BY c DESC LIMIT 10;"
        "SELECT UserID, COUNT(*) FROM hits GROUP BY UserID ORDER BY COUNT(*) DESC LIMIT 10;"
        "SELECT UserID, SearchPhrase, COUNT(*) FROM hits GROUP BY UserID, SearchPhrase ORDER BY COUNT(*) DESC LIMIT 10;"
        "SELECT UserID, SearchPhrase, COUNT(*) FROM hits GROUP BY UserID, SearchPhrase LIMIT 10;"
        "SELECT UserID, extract(minute FROM EventTime) AS m, SearchPhrase, COUNT(*) FROM hits GROUP BY UserID, m, SearchPhrase ORDER BY COUNT(*) DESC LIMIT 10;"
        "SELECT UserID FROM hits WHERE UserID = 435090932899640449;"
        "SELECT COUNT(*) FROM hits WHERE URL LIKE '%google%';"
        "SELECT SearchPhrase, MIN(URL), COUNT(*) AS c FROM hits WHERE URL LIKE '%google%' AND SearchPhrase <> '' GROUP BY SearchPhrase ORDER BY c DESC LIMIT 10;"
        "SELECT SearchPhrase, MIN(URL), MIN(Title), COUNT(*) AS c, COUNT(DISTINCT UserID) FROM hits WHERE Title LIKE '%Google%' AND URL NOT LIKE '%.google.%' AND SearchPhrase <> '' GROUP BY SearchPhrase ORDER BY c DESC LIMIT 10;"
        "SELECT * FROM hits WHERE URL LIKE '%google%' ORDER BY EventTime LIMIT 10;"
        "SELECT SearchPhrase FROM hits WHERE SearchPhrase <> '' ORDER BY EventTime LIMIT 10;"
        "SELECT SearchPhrase FROM hits WHERE SearchPhrase <> '' ORDER BY SearchPhrase LIMIT 10;"
        "SELECT SearchPhrase FROM hits WHERE SearchPhrase <> '' ORDER BY EventTime, SearchPhrase LIMIT 10;"
        "SELECT CounterID, AVG(length(URL)) AS l, COUNT(*) AS c FROM hits WHERE URL <> '' GROUP BY CounterID HAVING COUNT(*) > 100000 ORDER BY l DESC LIMIT 25;"
        "SELECT REGEXP_REPLACE(Referer, '^https?://(?:www\\.)?([^/]+)/.*$', '\1') AS k, AVG(length(Referer)) AS l, COUNT(*) AS c, MIN(Referer) FROM hits WHERE Referer <> '' GROUP BY k HAVING COUNT(*) > 100000 ORDER BY l DESC LIMIT 25;"
        "SELECT SUM(ResolutionWidth), SUM(ResolutionWidth + 1), SUM(ResolutionWidth + 2), SUM(ResolutionWidth + 3), SUM(ResolutionWidth + 4), SUM(ResolutionWidth + 5), SUM(ResolutionWidth + 6), SUM(ResolutionWidth + 7), SUM(ResolutionWidth + 8), SUM(ResolutionWidth + 9), SUM(ResolutionWidth + 10), SUM(ResolutionWidth + 11), SUM(ResolutionWidth + 12), SUM(ResolutionWidth + 13), SUM(ResolutionWidth + 14), SUM(ResolutionWidth + 15), SUM(ResolutionWidth + 16), SUM(ResolutionWidth + 17), SUM(ResolutionWidth + 18), SUM(ResolutionWidth + 19), SUM(ResolutionWidth + 20), SUM(ResolutionWidth + 21), SUM(ResolutionWidth + 22), SUM(ResolutionWidth + 23), SUM(ResolutionWidth + 24), SUM(ResolutionWidth + 25), SUM(ResolutionWidth + 26), SUM(ResolutionWidth + 27), SUM(ResolutionWidth + 28), SUM(ResolutionWidth + 29), SUM(ResolutionWidth + 30), SUM(ResolutionWidth + 31), SUM(ResolutionWidth + 32), SUM(ResolutionWidth + 33), SUM(ResolutionWidth + 34), SUM(ResolutionWidth + 35), SUM(ResolutionWidth + 36), SUM(ResolutionWidth + 37), SUM(ResolutionWidth + 38), SUM(ResolutionWidth + 39), SUM(ResolutionWidth + 40), SUM(ResolutionWidth + 41), SUM(ResolutionWidth + 42), SUM(ResolutionWidth + 43), SUM(ResolutionWidth + 44), SUM(ResolutionWidth + 45), SUM(ResolutionWidth + 46), SUM(ResolutionWidth + 47), SUM(ResolutionWidth + 48), SUM(ResolutionWidth + 49), SUM(ResolutionWidth + 50), SUM(ResolutionWidth + 51), SUM(ResolutionWidth + 52), SUM(ResolutionWidth + 53), SUM(ResolutionWidth + 54), SUM(ResolutionWidth + 55), SUM(ResolutionWidth + 56), SUM(ResolutionWidth + 57), SUM(ResolutionWidth + 58), SUM(ResolutionWidth + 59), SUM(ResolutionWidth + 60), SUM(ResolutionWidth + 61), SUM(ResolutionWidth + 62), SUM(ResolutionWidth + 63), SUM(ResolutionWidth + 64), SUM(ResolutionWidth + 65), SUM(ResolutionWidth + 66), SUM(ResolutionWidth + 67), SUM(ResolutionWidth + 68), SUM(ResolutionWidth + 69), SUM(ResolutionWidth + 70), SUM(ResolutionWidth + 71), SUM(ResolutionWidth + 72), SUM(ResolutionWidth + 73), SUM(ResolutionWidth + 74), SUM(ResolutionWidth + 75), SUM(ResolutionWidth + 76), SUM(ResolutionWidth + 77), SUM(ResolutionWidth + 78), SUM(ResolutionWidth + 79), SUM(ResolutionWidth + 80), SUM(ResolutionWidth + 81),SUM(ResolutionWidth + 82), SUM(ResolutionWidth + 83), SUM(ResolutionWidth + 84), SUM(ResolutionWidth + 85), SUM(ResolutionWidth + 86), SUM(ResolutionWidth + 87), SUM(ResolutionWidth + 88), SUM(ResolutionWidth + 89) FROM hits;"
        "SELECT SearchEngineID, ClientIP, COUNT(*) AS c, SUM(IsRefresh), AVG(ResolutionWidth) FROM hits WHERE SearchPhrase <> '' GROUP BY SearchEngineID, ClientIP ORDER BY c DESC LIMIT 10;"
        "SELECT WatchID, ClientIP, COUNT(*) AS c, SUM(IsRefresh), AVG(ResolutionWidth) FROM hits WHERE SearchPhrase <> '' GROUP BY WatchID, ClientIP ORDER BY c DESC LIMIT 10;"
        "SELECT WatchID, ClientIP, COUNT(*) AS c, SUM(IsRefresh), AVG(ResolutionWidth) FROM hits GROUP BY WatchID, ClientIP ORDER BY c DESC LIMIT 10;"
        "SELECT URL, COUNT(*) AS c FROM hits GROUP BY URL ORDER BY c DESC LIMIT 10;"
        "SELECT 1, URL, COUNT(*) AS c FROM hits GROUP BY 1, URL ORDER BY c DESC LIMIT 10;"
        "SELECT ClientIP, ClientIP - 1, ClientIP - 2, ClientIP - 3, COUNT(*) AS c FROM hits GROUP BY ClientIP, ClientIP - 1, ClientIP - 2, ClientIP - 3 ORDER BY c DESC LIMIT 10;"
        "SELECT URL, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= '2013-07-01' AND EventDate <= '2013-07-31' AND DontCountHits = 0 AND IsRefresh = 0 AND URL <> '' GROUP BY URL ORDER BY PageViews DESC LIMIT 10;"
        "SELECT Title, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= '2013-07-01' AND EventDate <= '2013-07-31' AND DontCountHits = 0 AND IsRefresh = 0 AND Title <> '' GROUP BY Title ORDER BY PageViews DESC LIMIT 10;"
        "SELECT URL, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= '2013-07-01' AND EventDate <= '2013-07-31' AND IsRefresh = 0 AND IsLink <> 0 AND IsDownload = 0 GROUP BY URL ORDER BY PageViews DESC LIMIT 10 OFFSET 1000;"
        "SELECT TraficSourceID, SearchEngineID, AdvEngineID, CASE WHEN (SearchEngineID = 0 AND AdvEngineID = 0) THEN Referer ELSE '' END AS Src, URL AS Dst, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= '2013-07-01' AND EventDate <= '2013-07-31' AND IsRefresh = 0 GROUP BY TraficSourceID, SearchEngineID, AdvEngineID, Src, Dst ORDER BY PageViews DESC LIMIT 10 OFFSET 1000;"
        "SELECT URLHash, EventDate, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= '2013-07-01' AND EventDate <= '2013-07-31' AND IsRefresh = 0 AND TraficSourceID IN (-1, 6) AND RefererHash = 3594120000172545465 GROUP BY URLHash, EventDate ORDER BY PageViews DESC LIMIT 10 OFFSET 100;"
        "SELECT WindowClientWidth, WindowClientHeight, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= '2013-07-01' AND EventDate <= '2013-07-31' AND IsRefresh = 0 AND DontCountHits = 0 AND URLHash = 2868770270353813622 GROUP BY WindowClientWidth, WindowClientHeight ORDER BY PageViews DESC LIMIT 10 OFFSET 10000;"
        "SELECT DATE_FORMAT(EventTime, '%Y-%m-%d %H:00:00') AS M, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= '2013-07-14' AND EventDate <= '2013-07-15' AND IsRefresh = 0 AND DontCountHits = 0 GROUP BY DATE_FORMAT(EventTime, '%Y-%m-%d %H:00:00') ORDER BY DATE_FORMAT(EventTime, '%Y-%m-%d %H:00:00') LIMIT 10 OFFSET 1000;"
    )
    
    # Initialize total_time variable
    total_time=0
    
    # Loop through the array of SQL queries
    for i in "${!sql_queries[@]}"; do
        echo -n "Q$((i + 1)): "
    
        min_time=999999  # Set a high initial value
        # Execute the SQL three times
        for j in {1..3}; do
            TIMEFORMAT=%R;
            exec_time=$( (time mysql -h127.0.0.1 -P $serverPort -uusername -Dclickbench -Ac -e "${sql_queries[i]}" > /dev/null) 2>&1 )
    
            # Check for errors in execution
            last_status=$?
            if [ $last_status -ne 0 ]; then
                echo "Error executing Q$((i + 1))"
                continue 2  # Skip to next query
            fi
    
            # Get the execution time (in seconds)
            exec_time=$(echo $exec_time | awk '{print $NF}')
    
            # Update min_time if current exec_time is smaller
            if (( $(echo "$exec_time < $min_time" | bc -l) )); then
                min_time=$exec_time
            fi
        done
    
        if [ $min_time == 999999 ]; then
            echo "No valid execution time"
        else
            echo $min_time
            # Add the min_time to total_time
            total_time=$(echo "$total_time + $min_time" | bc)
        fi
    done
    
    # Print the total execution time
    echo "Total execution time: $total_time"
  2. Execute the sh click.sh command to run the click.sh script.

Test results

Note

The results in the following table are in seconds.

Query

Specification of the read-only columnar instance

2 × 4 cores, 32 GB memory

2 × 8 cores, 32 GB memory

4 × 8 cores, 32 GB memory

2 × 16 cores, 64 GB memory

4 × 16 cores, 64 GB memory

SQL1

0.106

0.121

0.095

0.14

0.078

SQL2

0.076

0.073

0.058

0.085

0.052

SQL3

0.712

0.399

0.229

0.32

0.178

SQL4

0.554

0.257

0.181

0.212

0.141

SQL5

0.606

0.258

0.19

0.128

0.153

SQL6

1.589

0.515

0.343

0.559

0.276

SQL7

0.235

0.224

0.09

0.128

0.081

SQL8

0.067

0.072

0.06

0.087

0.055

SQL9

0.805

0.432

0.274

0.187

0.189

SQL10

7.438

0.825

0.484

0.603

0.268

SQL11

0.589

0.19

0.096

0.095

0.083

SQL12

0.553

0.316

0.22

0.45

0.165

SQL13

1.442

0.793

0.349

0.462

0.298

SQL14

1.965

0.906

0.401

0.59

0.345

SQL15

1.726

0.944

0.936

0.508

0.585

SQL16

0.536

0.402

0.211

0.137

0.192

SQL17

1.84

1.396

0.591

0.445

0.345

SQL18

1.84

1.188

0.533

0.322

0.305

SQL19

4.71

2.542

1.005

1.076

0.639

SQL20

0.017

0.018

0.018

0.012

0.018

SQL21

0.368

0.23

0.125

0.128

0.122

SQL22

0.457

0.308

0.169

0.161

0.14

SQL23

1.464

0.807

0.275

0.272

0.234

SQL24

1.127

0.991

0.393

1.67

0.32

SQL25

0.147

0.102

0.062

0.065

0.054

SQL26

0.261

0.148

0.081

0.112

0.078

SQL27

0.267

0.173

0.082

0.109

0.077

SQL28

1.502

1.11

0.516

0.672

0.401

SQL29

13.023

9.534

4.633

6.325

3.603

SQL30

0.404

0.252

0.181

0.212

0.136

SQL31

0.996

0.557

0.303

0.351

0.23

SQL32

1.527

0.941

0.38

0.458

0.303

SQL33

10.873

7.203

2.551

2.875

1.754

SQL34

17.157

5.941

3.428

3.256

2.326

SQL35

18.025

5.674

3.485

3.384

2.414

SQL36

0.72

0.432

0.223

0.321

0.228

SQL37

0.103

0.087

0.056

0.069

0.05

SQL38

0.054

0.048

0.038

0.052

0.039

SQL39

0.04

0.088

0.033

0.049

0.034

SQL40

0.24

0.181

0.094

0.11

0.091

SQL41

0.139

0.117

0.035

0.055

0.04

SQL42

0.137

0.132

0.068

0.208

0.067

SQL43

0.059

0.063

0.05

0.044

0.038

Total

96.496

46.99

23.625

27.504

17.225