PolarDB:ClickBench tests

Test data

The dataset is a web analytics event log with the following schema:

• Table: hits, with 105 columns (19 INTEGER, 6 BIGINT, 48 SMALLINT, 26 TEXT, 1 VARCHAR, 1 TIMESTAMP, 1 DATE)

• Row count: 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	…
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	…

SQL query 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

The test measures performance in column store mode. Only read-only columnar instance specifications are listed below. Test results are not affected by the version or specifications of the primary instance. To avoid memory overflow and test failure, make sure each read-only columnar instance has at least 32 GB of memory.

For setup instructions, see Create a PolarDB-X instance, Change instance specifications, Add a read-only column store instance, and Add or remove nodes.

ECS instance specifications

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

Prerequisites

Before you begin, ensure that you have:

• A PolarDB-X instance with at least one read-only columnar instance (minimum 32 GB memory per node)

• An ECS instance (ecs.g8i.16xlarge or equivalent) with JDK 11 installed

• The batch-tool.jar binary — see Use Batch Tool to export and import data

• Network connectivity from the ECS instance to the PolarDB-X primary instance

Preparations

Step 1: Download and decompress the dataset

Run the following commands on the ECS instance:

wget https://datasets.clickhouse.com/hits_compatible/hits.tsv.gz
gunzip hits.tsv.gz

The decompressed file hits.tsv is approximately 70 GB. Verify the file is intact before proceeding:

wc -l hits.tsv
# Expected: approximately 100,000,000 lines

Step 2: Create the database and table

Connect to the primary PolarDB-X instance and run:

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;

Step 3: Import data

Save the following command to a file named load.sh on the ECS instance, then run sh load.sh:

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

For Batch Tool installation and usage, see Use Batch Tool to export and import data.

The key parameters are:

Adjust the parameters above based on your business requirements.

After the import completes, verify the row count:

SELECT COUNT(*) FROM clickbench.hits;
-- Expected: approximately 100,000,000

Step 4: Create a clustered columnar index (CCI)

In the clickbench database, run:

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

For more information, see CCI.

Database parameter tuning

Apply the following global settings before running the benchmark. These parameters reduce overhead from logging, profiling, and background statistics collection, and tune MPP (Massively Parallel Processing) memory buffers for maximum analytical throughput.

-- Disable SQL logging and CPU profiling to reduce overhead
SET GLOBAL RECORD_SQL = false;
SET GLOBAL MPP_METRIC_LEVEL = 0;
SET GLOBAL ENABLE_CPU_PROFILE = false;

-- Disable background statistics collection and feedback
SET GLOBAL ENABLE_BACKGROUND_STATISTIC_COLLECTION = false;
SET GLOBAL ENABLE_STATISTIC_FEEDBACK = false;

-- MPP memory buffer sizing (32 GB each)
-- Increase these buffers to allow large intermediate results to stay in memory
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;

-- Enable MPP chunk compression to reduce network transfer size
SET GLOBAL ENABLE_MPP_SERIALIZED_CHUNK_COMPRESSION = true;

-- Disable OSS-compatible mode for maximum local performance
SET GLOBAL ENABLE_OSS_COMPATIBLE = false;

-- Enable MPP aggregation optimizations
SET GLOBAL ENABLE_STREAM_PARTIAL_AGG = true;
SET GLOBAL ENABLE_TRANSPARENT_PARTIAL_AGG = true;
SET GLOBAL ENABLE_SIMPLIFY_GROUP_BY_RULE = true;

Run the test script

1. Save the following script to a file named click.sh on the ECS instance. Replace the connection parameters (-h, -P, -u, -D) with your instance values.

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 each SQL query three times; record the minimum execution time
    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"

1. Run sh click.sh to execute the script.