一、开发环境

OpenJDK版本 >= 17
ClickHouse：20.7+

1、支持的数据类型

Format	Support	Comment
AggregatedFunction	❌	limited to `groupBitmap`, and known to have issue with 64bit bitmap
Array(*)	✅
Bool	✅
Date*	✅
DateTime*	✅
Decimal*	✅	`SET output_format_decimal_trailing_zeros=1` in 21.9+ for consistency
Enum*	✅	can be treated as both string and integer
Geo Types	✅	Point, Ring, Polygon, and MultiPolygon
Int, UInt	✅	UInt64 is mapped to `long`
IPv*	✅
Map(*)	✅
Nested(*)	✅
Object('JSON')	✅
SimpleAggregateFunction	✅
*String	✅
Tuple(*)	✅
UUID	✅

二、Java客户端方式

1、引入依赖

<dependency>
    <groupId>com.clickhouse</groupId>
    <!-- or clickhouse-grpc-client if you prefer gRPC -->
    <artifactId>clickhouse-http-client</artifactId>
    <version>0.4.0</version>
</dependency>

2、连接ClickHouse

连接字符串: protocol://host[:port][/database][?param[=value][&param[=value]][#tag[,tag]]

示例:

http://localhost:8443?ssl=true&sslmode=NONE
http://(https://[email protected]:443
tcp://localhost?!auto_discovery#experimental),(grpc://localhost#experimental)?failover=3#test

ClickHouseNodes servers = ClickHouseNodes.of(
    "jdbc:ch:http://server1.domain,server2.domain,server3.domain/my_db"
    + "?load_balancing_policy=random&health_check_interval=5000&failover=2");

3、查询

ClickHouseResponse response = client.connect(endpoint) // or client.connect(endpoints)
    // you'll have to parse response manually if using a different format
    .format(ClickHouseFormat.RowBinaryWithNamesAndTypes)
    .query("select * from numbers(:limit)")
    .params(1000).executeAndWait()) {
            ClickHouseResponseSummary summary = response.getSummary();
            long totalRows = summary.getTotalRowsToRead();

4、流式查询

ClickHouseResponse response = client.connect(endpoint) // or client.connect(endpoints)
    // you'll have to parse response manually if using a different format
    .format(ClickHouseFormat.RowBinaryWithNamesAndTypes)
    .query("select * from numbers(:limit)")
    .params(1000).executeAndWait()) {
    for (ClickHouseRecord r : response.records()) {
        int num = r.getValue(0).asInteger();
        // type conversion
        String str = r.getValue(0).asString();
        LocalDate date = r.getValue(0).asDate();
    }

5、Insert

try (ClickHouseClient client = ClickHouseClient.newInstance(ClickHouseProtocol.HTTP)) {
    ClickHouseRequest<?> request = client.connect(servers).format(ClickHouseFormat.RowBinaryWithNamesAndTypes);
    // load data into a table and wait until it's completed
    request.write()
        .query("insert into my_table select c2, c3 from input('c1 UInt8, c2 String, c3 Int32')")
        .data(myInputStream).execute().thenAccept(response -> {
            response.close();
        });

6、多语句操作

在同一会话中一个接一个地在工作线程中执行多个语句：

CompletableFuture<List<ClickHouseResponseSummary>> future = ClickHouseClient.send(servers.get(),
    "create database if not exists my_base",
    "use my_base",
    "create table if not exists test_table(s String) engine=Memory",
    "insert into test_table values('1')('2')('3')",
    "select * from test_table limit 1",
    "truncate table test_table",
    "drop table if exists test_table");

// block current thread until queries completed, and then retrieve summaries
List<ClickHouseResponseSummary> results = future.get();

三、JDBC Driver方式

clickhouse jdbc实现了标准jdbc接口。它构建在clickhouse客户端之上，提供了自定义类型映射、事务支持、标准同步UPDATE和DELETE语句等附加功能，因此可以轻松地与遗留应用程序和工具一起使用。
clickhouse jdbc API是同步的，通常会有更多的开销（例如SQL解析和类型映射/转换等）。当性能至关重要时，或者如果您喜欢更直接的访问clickhouse的方式，请考虑使用clickhouse客户端。

1、引入依赖

<dependency>
    <groupId>com.clickhouse</groupId>
    <artifactId>clickhouse-jdbc</artifactId>
    <version>0.4.0</version>
    <!-- use uber jar with all dependencies included, change classifier to http for smaller jar -->
    <classifier>all</classifier>
    <exclusions>
        <exclusion>
            <groupId>*</groupId>
            <artifactId>*</artifactId>
        </exclusion>
    </exclusions>
</dependency>

2、配置

驱动程序类：com.clickhouse.jdbc.ClickHouseDriver

连接字符串：例如：jdbc:(ch|clickhouse)[:<protocol>]://endpoint1[,endpoint2,...][/<database>][?param1=value1&param2=value2][#tag1,tag2,...]

jdbc:ch://localhost与jdbc:clickhouse:http://localhost:8123
jdbc:ch:https://localhost与jdbc:clickhouse:http://localhost:8443?ssl=true&sslmode=STRICT
jdbc:ch:grpc://localhost与jdbc:clickhouse:grpc://localhost:9100

连接属性：

属性	违约	描述
continueBatchOnError	`false`	发生错误时是否继续批处理
createDatabaseIfNotExist	`false`	如果数据库不存在，是否创建数据库
custom_http_headers		逗号分隔的自定义 HTTP 标头，例如：User-Agent=client1,X-Gateway-Id=123
custom_http_params		逗号分隔的自定义 HTTP 标头，例如：User-Agent=client1,X-Gateway-Id=123
nullAsDefault	`0`	0-将null值按原样处理，并在将null插入不可为null的列时引发异常；1-按原样处理null值，并禁用插入时的null检查；2-将null替换为查询和插入的相应数据类型的默认值
jdbcCompliance	`true`	是否支持标准同步 UPDATE/DELETE 和fake transaction
typeMappings		自定义ClickHouse数据类型和Java类之间的映射，这将影响getColumnType（）和getObject（class<？>）的结果。例如：UInt128=java.lang.String，UInt256=java.lang.String
wrapperObject	`false`	getObject（）是否应为Array/Tuple返回java.sql.Array/java.sql.Struct。

3、连接到ClickHouse

String url = "jdbc:ch://my-server/system"; // use http protocol and port 8123 by default
// String url = "jdbc:ch://my-server:8443/system?ssl=true&sslmode=strict&&sslrootcert=/mine.crt";
Properties properties = new Properties();
// properties.setProperty("ssl", "true");
// properties.setProperty("sslmode", "NONE"); // NONE to trust all servers; STRICT for trusted only
ClickHouseDataSource dataSource = new ClickHouseDataSource(url, new Properties());
try (Connection conn = dataSource.getConnection("default", "password");
    Statement stmt = conn.createStatement()) {
}

4、Query


try (Connection conn = dataSource.getConnection(...);
    Statement stmt = conn.createStatement()) {
    ResultSet rs = stmt.executeQuery("select * from numbers(50000)");
    while(rs.next()) {
        // ...
    }
}

5、Insert

使用 input table函数

具有最佳性能的推荐方式

try (PreparedStatement ps = conn.prepareStatement(
    "insert into mytable select col1, col2 from input('col1 String, col2 DateTime64(3), col3 Int32')")) {
    // the column definition will be parsed so the driver knows there are 3 parameters: col1, col2 and col3
    ps.setString(1, "test"); // col1
    ps.setObject(2, LocalDateTime.now()); // col2, setTimestamp is slow and not recommended
    ps.setInt(3, 123); // col3
    ps.addBatch(); // parameters will be write into buffered stream immediately in binary format
    ...
    ps.executeBatch(); // stream everything on-hand into ClickHouse
}

Insert

它更易于使用，但与输入函数相比，性能更慢

try (PreparedStatement ps = conn.prepareStatement("insert into mytable(* except (description))")) {
    // the driver will issue query "select * except (description) from mytable where 0" for type inferring
    // since description column is excluded, we know there are only two parameters: col1 and col2
    ps.setString(1, "test"); // id
    ps.setObject(2, LocalDateTime.now()); // timestamp
    ps.addBatch(); // parameters will be write into buffered stream immediately in binary format
    ...
    ps.executeBatch(); // stream everything on-hand into ClickHouse
}

使用占位符新增

不推荐，因为它基于大型 SQL

// Note: "insert into mytable values(?,?,?)" is treated as "insert into mytable"
try (PreparedStatement ps = conn.prepareStatement("insert into mytable values(trim(?),?,?)")) {
    ps.setString(1, "test"); // id
    ps.setObject(2, LocalDateTime.now()); // timestamp
    ps.setString(3, null); // description
    ps.addBatch(); // append parameters to the query
    ...
    ps.executeBatch(); // issue the composed query: insert into mytable values(...)(...)...(...)
}

6、高级接口

处理日期时间和时区

请使用java.time.LocalDateTime或java.time.OffsetDateTime代替java.sql.Timestamp，并使用java.time_LocalDate代替java.sql.Date。

try (PreparedStatement ps = conn.prepareStatement("select date_time from mytable where date_time > ?")) {
    ps.setObject(2, LocalDateTime.now());
    ResultSet rs = ps.executeQuery();
    while(rs.next()) {
        LocalDateTime dateTime = (LocalDateTime) rs.getObject(1);
    }
    ...
}

7、处理聚合函数

// batch insert using input function
try (ClickHouseConnection conn = newConnection(props);
        Statement s = conn.createStatement();
        PreparedStatement stmt = conn.prepareStatement(
                "insert into test_batch_input select id, name, value from input('id Int32, name Nullable(String), desc Nullable(String), value AggregateFunction(groupBitmap, UInt32)')")) {
    s.execute("drop table if exists test_batch_input;"
            + "create table test_batch_input(id Int32, name Nullable(String), value AggregateFunction(groupBitmap, UInt32))engine=Memory");
    Object[][] objs = new Object[][] {
            new Object[] { 1, "a", "aaaaa", ClickHouseBitmap.wrap(1, 2, 3, 4, 5) },
            new Object[] { 2, "b", null, ClickHouseBitmap.wrap(6, 7, 8, 9, 10) },
            new Object[] { 3, null, "33333", ClickHouseBitmap.wrap(11, 12, 13) }
    };
    for (Object[] v : objs) {
        stmt.setInt(1, (int) v[0]);
        stmt.setString(2, (String) v[1]);
        stmt.setString(3, (String) v[2]);
        stmt.setObject(4, v[3]);
        stmt.addBatch();
    }
    int[] results = stmt.executeBatch();
    ...
}

// use bitmap as query parameter
try (PreparedStatement stmt = conn.prepareStatement(
    "SELECT bitmapContains(my_bitmap, toUInt32(1)) as v1, bitmapContains(my_bitmap, toUInt32(2)) as v2 from {tt 'ext_table'}")) {
    stmt.setObject(1, ClickHouseExternalTable.builder().name("ext_table")
            .columns("my_bitmap AggregateFunction(groupBitmap,UInt32)").format(ClickHouseFormat.RowBinary)
            .content(new ByteArrayInputStream(ClickHouseBitmap.wrap(1, 3, 5).toBytes()))
            .asTempTable()
            .build());
    ResultSet rs = stmt.executeQuery();
    Assert.assertTrue(rs.next());
    Assert.assertEquals(rs.getInt(1), 1);
    Assert.assertEquals(rs.getInt(2), 0);
    Assert.assertFalse(rs.next());
}

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Java ClickHouse整合—官方教程

一、开发环境

1、支持的数据类型

二、Java客户端方式

1、引入依赖

2、连接ClickHouse

3、查询

4、流式查询

5、Insert

6、多语句操作

三、JDBC Driver方式

1、引入依赖

2、配置

3、连接到ClickHouse

4、Query

5、Insert

6、高级接口

7、处理聚合函数

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