This topic describes how to use TPC-H to test the online analytical processing (OLAP) performance of a PolarDB for MySQL 8.0 cluster of Cluster Edition. You can follow the instructions in this topic to test the performance of the database.

Introduction to parallel query

PolarDB for MySQL 8.0 Cluster Edition launches a parallel query framework. By default, parallel query is disabled. After you enable the feature, if the amount of the queried data reaches a specific threshold, parallel queries are automatically performed. This exponentially reduces query response time.

Note You can specify the loose_max_parallel_degree parameter to enable parallel query. For more information, see Specify cluster parameters.

The following items describe the loose_max_parallel_degree parameter:

  • The minimum value is 0. This value indicates that parallel query is disabled.
  • The maximum value is 1024.
  • We recommend that you set the parallel query parameter (loose_max_parallel_degree ) to 16.

PolarDB for MySQL 8.0 clusters of Cluster Edition distribute data shards to different threads in the storage. Multiple threads perform parallel computing and return the results to the leader thread. Then, the leader thread merges and returns the results to clients. This improves the efficiency of queries.

Parallel queries are achieved based on the parallel processing capabilities of multi-core CPUs. The following figure shows parallel processing in a cluster that uses an 8-Core 32 GB specification.

Figure

The following sections describe the test method and results of the performance for a PolarDB cluster when the parallel query parameter is set to 16 and 0.

Test environment

  • The Elastic Computing Service (ECS) instance and the PolarDB cluster to be tested must be deployed in the same zone of the same region. In this example, Hangzhou Zone I in the China (Hangzhou) region is used.
  • The network type is virtual private cloud (VPC).
    Note Make sure that the ECS instance and the PolarDB cluster are in the same VPC.
  • The following PolarDB cluster is used in the test:
    • The node specification is polar.mysql.x8.4xlarge (32-core 256 GB).
    • The database is MySQL 8.0.
    • The service edition of the cluster is Cluster Edition.
    • Two nodes, one primary node and one read-only node, are available.
    • The primary endpoint is used as the connection string. For more information about how to view the primary endpoint of a PolarDB cluster, see View or apply for an endpoint.
  • The following ECS instance is used in the test:
    • The instance type is ecs.c5.4xlarge.
    • An ultra disk of 1,000 GB is attached to the instance.
    • The image of the instance is 64-bit CentOS 7.0.

Test tool

TPC-H is a standard benchmark. It is developed and released by the Transaction Processing Performance Council (TPC) to evaluate the query capabilities of databases. The TPC-H benchmark contains 8 tables and 22 complex structured query language (SQL) statements. Most of the queries contain JOINs on several tables, subqueries, and GROUP BY clauses.

Install TPC-H

  1. Install TPC-H on the ECS instance.
    Note
    • TPC-H_Tools_v2.18.0 is used in this topic.
    • You must finish the registration before you download TPC-H.
  2. Change the current working directory to dbgen.
    cd dbgen
  3. Copy the makefile file.
    cp makefile.suite makefile
  4. Modify parameters, such as CC, DATABASE, MACHINE, and WORKLOAD, in the makefile file.
    1. Open the makefile file.
      vim makefile
    2. Modify the definitions of the CC, DATABASE, MACHINE, and WORKLOAD parameters.
        ################
        ## CHANGE NAME OF ANSI COMPILER HERE
        ################
        CC      = gcc
        # Current values for DATABASE are: INFORMIX, DB2, ORACLE,
        #                                  SQLSERVER, SYBASE, TDAT (Teradata)
        # Current values for MACHINE are:  ATT, DOS, HP, IBM, ICL, MVS,
        #                                  SGI, SUN, U2200, VMS, LINUX, WIN32
        # Current values for WORKLOAD are:  TPCH
        DATABASE= MYSQL
        MACHINE = LINUX
        WORKLOAD = TPCH
    3. Press ECS and enter :wq to save the file and exit.
  5. Modify the tpcd.h file and add new macro definitions.
    1. Open the tpcd.h file.
      vim tpcd.h
    2. Add the following macro definitions:
      #ifdef MYSQL
      #define GEN_QUERY_PLAN ""
      #define START_TRAN "START TRANSACTION"
      #define END_TRAN "COMMIT"
      #define SET_OUTPUT ""
      #define SET_ROWCOUNT "limit %d;\n"
      #define SET_DBASE "use %s;\n"
      #endif
    3. Press ECS and enter :wq to save the file and exit.
  6. Compile the file.
    make

    After the file is compiled, the following executable files are generated in the directory:

    • dbgen: the tool that is used to generate data. When you use the InfiniDB test script, you must use this tool to generate data for TPC-H.
    • qgen: the tool that is used to generate SQL statements. This tool generates initial statements for the test. The queries generated by different seeds are different. To ensure repeatability, use the 22 queries provided in the attachment.
  7. Use TPC-H to generate test data.
    ./dbgen -s 100

    The -s parameter specifies the number of repositories that are used to generate data.

  8. Use TPC-H to generate a query.
    Note To ensure the repeatability of test results, you can skip this step and click here to download and use the 22 queries.
    1. Copy qgen and dists.dss into the queries directory.
      cp qgen queries
      cp dists.dss queries
    2. Use the following script to generate a query.
      #!/usr/bin/bash
      for i in {1..22}
      do  
        ./qgen -d $i -s 100 > db"$i".sql
      done

Test method

  1. Specify the loose_max_parallel_degree parameter to enable or disable parallel query.
    Note The test shows how queries are performed when xxx is enabled and disabled. You can set the loose_max_parallel_degree parameters to 16 and 0 respectively to obtain different results. For more information, see Specify cluster parameters.
  2. Connect to the PolarDB database from the ECS instance. For more information, see Connect to a PolarDB for MySQL cluster.
  3. Create a database.
    create database tpch100g
  4. Create a table.
    source ./dss.ddl
    Note dss.ddl is in the dbgen directory of TPC-H.
  5. Load data.
    1. Run the following script to create load.ddl:
      load data local INFILE 'customer.tbl' INTO TABLE customer FIELDS TERMINATED BY '|';
      load data local INFILE 'region.tbl' INTO TABLE region FIELDS TERMINATED BY '|';
      load data local INFILE 'nation.tbl' INTO TABLE nation FIELDS TERMINATED BY '|';
      load data local INFILE 'supplier.tbl' INTO TABLE supplier FIELDS TERMINATED BY '|';
      load data local INFILE 'part.tbl' INTO TABLE part FIELDS TERMINATED BY '|';
      load data local INFILE 'partsupp.tbl' INTO TABLE partsupp FIELDS TERMINATED BY '|';
      load data local INFILE 'orders.tbl' INTO TABLE orders FIELDS TERMINATED BY '|';
      load data local INFILE 'lineitem.tbl' INTO TABLE lineitem FIELDS TERMINATED BY '|';
    2. Load data.
      source ./load.ddl
  6. Create primary keys and foreign keys.
    source ./dss.ri

    The tpch100g database that was previously created is used in the example. Replace the content in the dss.ri file in TPC-H with the following content:

    use TPCH100G;
    -- ALTER TABLE REGION DROP PRIMARY KEY;
    -- ALTER TABLE NATION DROP PRIMARY KEY;
    -- ALTER TABLE PART DROP PRIMARY KEY;
    -- ALTER TABLE SUPPLIER DROP PRIMARY KEY;
    -- ALTER TABLE PARTSUPP DROP PRIMARY KEY;
    -- ALTER TABLE ORDERS DROP PRIMARY KEY;
    -- ALTER TABLE LINEITEM DROP PRIMARY KEY;
    -- ALTER TABLE CUSTOMER DROP PRIMARY KEY;
    -- For table REGION
    ALTER TABLE REGION
    ADD PRIMARY KEY (R_REGIONKEY);
    -- For table NATION
    ALTER TABLE NATION
    ADD PRIMARY KEY (N_NATIONKEY);
    ALTER TABLE NATION
    ADD FOREIGN KEY NATION_FK1 (N_REGIONKEY) references REGION(R_REGIONKEY);
    COMMIT WORK;
    -- For table PART
    ALTER TABLE PART
    ADD PRIMARY KEY (P_PARTKEY);
    COMMIT WORK;
    -- For table SUPPLIER
    ALTER TABLE SUPPLIER
    ADD PRIMARY KEY (S_SUPPKEY);
    ALTER TABLE SUPPLIER
    ADD FOREIGN KEY SUPPLIER_FK1 (S_NATIONKEY) references NATION(N_NATIONKEY);
    COMMIT WORK;
    -- For table PARTSUPP
    ALTER TABLE PARTSUPP
    ADD PRIMARY KEY (PS_PARTKEY,PS_SUPPKEY);
    COMMIT WORK;
    -- For table CUSTOMER
    ALTER TABLE CUSTOMER
    ADD PRIMARY KEY (C_CUSTKEY);
    ALTER TABLE CUSTOMER
    ADD FOREIGN KEY CUSTOMER_FK1 (C_NATIONKEY) references NATION(N_NATIONKEY);
    COMMIT WORK;
    -- For table LINEITEM
    ALTER TABLE LINEITEM
    ADD PRIMARY KEY (L_ORDERKEY,L_LINENUMBER);
    COMMIT WORK;
    -- For table ORDERS
    ALTER TABLE ORDERS
    ADD PRIMARY KEY (O_ORDERKEY);
    COMMIT WORK;
    -- For table PARTSUPP
    ALTER TABLE PARTSUPP
    ADD FOREIGN KEY PARTSUPP_FK1 (PS_SUPPKEY) references SUPPLIER(S_SUPPKEY);
    COMMIT WORK;
    ALTER TABLE PARTSUPP
    ADD FOREIGN KEY PARTSUPP_FK2 (PS_PARTKEY) references PART(P_PARTKEY);
    COMMIT WORK;
    -- For table ORDERS
    ALTER TABLE ORDERS
    ADD FOREIGN KEY ORDERS_FK1 (O_CUSTKEY) references CUSTOMER(C_CUSTKEY);
    COMMIT WORK;
    -- For table LINEITEM
    ALTER TABLE LINEITEM
    ADD FOREIGN KEY LINEITEM_FK1 (L_ORDERKEY)  references ORDERS(O_ORDERKEY);
    COMMIT WORK;
    ALTER TABLE LINEITEM
    ADD FOREIGN KEY LINEITEM_FK2 (L_PARTKEY,L_SUPPKEY) references 
            PARTSUPP(PS_PARTKEY,PS_SUPPKEY);
    COMMIT WORK;
  7. Create indexes.
    #!/usr/bin/bash
    host=$1
    port=$2
    user=$3
    password=$4
    db=$5
    sqls=("create index i_s_nationkey on supplier (s_nationkey);"
    "create index i_ps_partkey on partsupp (ps_partkey);"
    "create index i_ps_suppkey on partsupp (ps_suppkey);"
    "create index i_c_nationkey on customer (c_nationkey);"
    "create index i_o_custkey on orders (o_custkey);"
    "create index i_o_orderdate on orders (o_orderdate);"
    "create index i_l_orderkey on lineitem (l_orderkey);"
    "create index i_l_partkey on lineitem (l_partkey);"
    "create index i_l_suppkey on lineitem (l_suppkey);"
    "create index i_l_partkey_suppkey on lineitem (l_partkey, l_suppkey);"
    "create index i_l_shipdate on lineitem (l_shipdate);"
    "create index i_l_commitdate on lineitem (l_commitdate);"
    "create index i_l_receiptdate on lineitem (l_receiptdate);"
    "create index i_n_regionkey on nation (n_regionkey);"
    "analyze table supplier"
    "analyze table part"
    "analyze table partsupp"
    "analyze table customer"
    "analyze table orders"
    "analyze table lineitem"
    "analyze table nation"
    "analyze table region")
    for sql in "${sqls[@]}"
    do
        mysql -h$host -P$port -u$user -p$password -D$db  -e "$sql"
    done
    Note To more effectively measure the performance improvement brought by parallel query, you can perform the following query to preload the indexes to the memory pool.
    #!/bin/bash
    host=$1
    port=$2
    user=$3
    password=$4
    dbname=$5
    MYSQL="mysql -h$host -P$port -u$user -p$password -D$dbname"
    if [ -z ${dbname} ]; then
        echo "dbname not defined."
        exit 1
    fi
    table_indexes=(
            "supplier PRIMARY"
            "supplier i_s_nationkey"
            "part PRIMARY"
            "partsupp PRIMARY"
            "partsupp i_ps_partkey"
            "partsupp i_ps_suppkey"
            "customer PRIMARY"
            "customer i_c_nationkey"
            "orders PRIMARY"
            "orders i_o_custkey"
            "orders i_o_orderdate"
            "lineitem PRIMARY"
            "lineitem i_l_orderkey"
            "lineitem i_l_partkey"
            "lineitem i_l_suppkey"
            "lineitem i_l_partkey_suppkey"
            "lineitem i_l_shipdate"
            "lineitem i_l_commitdate"
            "lineitem i_l_receiptdate"
            "nation i_n_regionkey"
            "nation PRIMARY"
            "region PRIMARY"
    )
    for table_index in "${table_indexes[@]}"
    do
        ti=($table_index)
        table=${ti[0]}
        index=${ti[1]}
        SQL="select count(*) from ${table} force index(${index})"
        echo "$MYSQL -e '$SQL'"
        $MYSQL -e "$SQL"
    done
  8. Perform queries.
    #!/usr/bin/env bash
    host=$1
    port=$2
    user=$3
    password=$4
    database=$5
    resfile=$6
    echo "start test run at"`date "+%Y-%m-%d %H:%M:%S"`|tee -a ${resfile}.out
    for (( i=1; i<=22;i=i+1 ))
    do
    queryfile="Q"${i}".sql"
    start_time=`date "+%s.%N"`
    echo "run query ${i}"|tee -a ${resfile}.out
    mysql -h ${host}  -P${port} -u${user} -p${password} $database -e" source $queryfile;" |tee -a ${resfile}.out
    end_time=`date "+%s.%N"`
    start_s=${start_time%.*}
    start_nanos=${start_time#*.}
    end_s=${end_time%.*}
    end_nanos=${end_time#*.}
    if [ "$end_nanos" -lt "$start_nanos" ];then
            end_s=$(( 10#$end_s -1 ))
            end_nanos=$(( 10#$end_nanos + 10 ** 9))
    fi
    time=$(( 10#$end_s - 10#$start_s )).`printf "%03d\n" $(( (10#$end_nanos - 10#$start_nanos)/10**6 ))`
    echo ${queryfile} "the "${j}" run cost "${time}" second start at"`date -d @$start_time "+%Y-%m-%d %H:%M:%S"`" stop at"`date -d @$end_time "+%Y-%m-%d %H:%M:%S"` >> ${resfile}.time
    done

Result

The following figures show the results when the parallel query parameter is set to 16 and 0.

ComparisonPerformance improvement
Note In the preceding figures, the letter Q is short for query. For example, Q1 represents the first query.

The following table describes the test results.

Query Time consumed (seconds)

Degree of parallelism (DOP) = 16

Time consumed (seconds)

DOP = 0

Performance increases by

(DOP 0/DOP 16)

Q1 80.18 1290.6 16
Q2 1.44 11.8 8
Q3 25.05 244.92 10
Q4 6.91 59.61 9
Q5 24.44 231.18 9
Q6 14.51 217.42 15
Q7 51.97 410.59 8
Q8 5.61 57.52 10
Q9 37.84 415.11 11
Q10 38.72 139.73 4
Q11 11.75 30.67 3
Q12 15.89 245.19 15
Q13 104.12 718.2 7
Q14 8.31 66.66 8
Q15 32.5 123.79 4
Q16 26.9 37.54 1
Q17 16.2 54.34 3
Q18 66.77 240.28 4
Q19 1.58 18.62 12
Q20 45.88 46.91 1
Q21 53.99 253.27 5
Q22 2.07 17.08 8