Version: nightly

Java

GreptimeDB offers ingester libraries for high-throughput data writing. It utilizes the gRPC protocol, which supports schemaless writing and eliminates the need to create tables before writing data. For more information, refer to Automatic Schema Generation.

The Java ingester SDK provided by GreptimeDB is a lightweight library with the following features:

SPI-based extensible network transport layer, which provides the default implementation using the gRPC framework.
Non-blocking, purely asynchronous API that is easy to use.
Automatic collection of various performance metrics by default. You can then configure and write them to local files.
Ability to take in-memory snapshots of critical objects, configure them, and write them to local files. This is helpful for troubleshooting complex issues.

Quick start demos

To quickly get started, you can explore the quick start demos to understand how to use the GreptimeDB Java ingester SDK.

Installation

Install the Java Development Kit(JDK)

Make sure that your system has JDK 8 or later installed. For more information on how to check your version of Java and install the JDK, see the Oracle Overview of JDK Installation documentation

Add GreptiemDB Java SDK as a Dependency

If you are using Maven, add the following to your pom.xml dependencies list:

<dependency>
    <groupId>io.greptime</groupId>
    <artifactId>ingester-all</artifactId>
    <version>0.9.1</version>
</dependency>

The latest version can be viewed here.

After configuring your dependencies, make sure they are available to your project. This may require refreshing the project in your IDE or running the dependency manager.

Connect to database

If you have set the --user-provider configuration when starting GreptimeDB, you will need to provide a username and password to connect to GreptimeDB. The following example shows how to set the username and password when using the library to connect to GreptimeDB.

The following code demonstrates how to connect to GreptimeDB with the simplest configuration. For customizing the connection options, please refer to API Documentation. Please pay attention to the accompanying comments for each option, as they provide detailed explanations of their respective roles.

// GreptimeDB has a default database named "public" in the default catalog "greptime",
// we can use it as the test database
String database = "public";
// By default, GreptimeDB listens on port 4001 using the gRPC protocol.
// We can provide multiple endpoints that point to the same GreptimeDB cluster.
// The client will make calls to these endpoints based on a load balancing strategy.
String[] endpoints = {"127.0.0.1:4001"};
// Sets authentication information.
AuthInfo authInfo = new AuthInfo("username", "password");
GreptimeOptions opts = GreptimeOptions.newBuilder(endpoints, database)
        // If the database does not require authentication, we can use AuthInfo.noAuthorization() as the parameter.
        .authInfo(authInfo)
        // Enable secure connection if your server is secured by TLS
        //.tlsOptions(new TlsOptions())
        // A good start ^_^
        .build();

GreptimeDB client = GreptimeDB.create(opts);

For customizing the connection options, please refer to API Documentation.

Data model

Each row item in a table consists of three types of columns: Tag, Timestamp, and Field. For more information, see Data Model. The types of column values could be String, Float, Int, Timestamp, JSON etc. For more information, see Data Types.

Low-level API

The GreptimeDB low-level API provides a straightforward method to write data to GreptimeDB by adding rows to the table object with a predefined schema.

Create row objects

This following code snippet begins by constructing a table named cpu_metric, which includes columns host, cpu_user, cpu_sys, and ts. Subsequently, it inserts a single row into the table.

The table consists of three types of columns:

Tag: The host column, with values of type String.
Field: The cpu_user and cpu_sys columns, with values of type Float.
Timestamp: The ts column, with values of type Timestamp.

// Construct the table schema for CPU metrics
TableSchema cpuMetricSchema = TableSchema.newBuilder("cpu_metric")
        .addTag("host", DataType.String) // Identifier for the host
        .addTimestamp("ts", DataType.TimestampMillisecond) // Timestamp in milliseconds
        .addField("cpu_user", DataType.Float64) // CPU usage by user processes
        .addField("cpu_sys", DataType.Float64) // CPU usage by system processes
        .build();

// Create the table from the defined schema
Table cpuMetric = Table.from(cpuMetricSchema);

// Example data for a single row
String host = "127.0.0.1"; // Host identifier
long ts = System.currentTimeMillis(); // Current timestamp
double cpuUser = 0.1; // CPU usage by user processes (in percentage)
double cpuSys = 0.12; // CPU usage by system processes (in percentage)

// Insert the row into the table
// NOTE: The arguments must be in the same order as the columns in the defined schema: host, ts, cpu_user, cpu_sys
cpuMetric.addRow(host, ts, cpuUser, cpuSys);

To improve the efficiency of writing data, you can create multiple rows at once to write to GreptimeDB.

// Creates schemas
TableSchema cpuMetricSchema = TableSchema.newBuilder("cpu_metric")
        .addTag("host", DataType.String)
        .addTimestamp("ts", DataType.TimestampMillisecond)
        .addField("cpu_user", DataType.Float64)
        .addField("cpu_sys", DataType.Float64)
        .build();

TableSchema memMetricSchema = TableSchema.newBuilder("mem_metric")
        .addTag("host", DataType.String)
        .addTimestamp("ts", DataType.TimestampMillisecond)
        .addField("mem_usage", DataType.Float64)
        .build();

Table cpuMetric = Table.from(cpuMetricSchema);
Table memMetric = Table.from(memMetricSchema);

// Adds row data items
for (int i = 0; i < 10; i++) {
    String host = "127.0.0." + i;
    long ts = System.currentTimeMillis();
    double cpuUser = i + 0.1;
    double cpuSys = i + 0.12;
    cpuMetric.addRow(host, ts, cpuUser, cpuSys);
}

for (int i = 0; i < 10; i++) {
    String host = "127.0.0." + i;
    long ts = System.currentTimeMillis();
    double memUsage = i + 0.2;
    memMetric.addRow(host, ts, memUsage);
}

Insert data

The following example shows how to insert rows to tables in GreptimeDB.

// Saves data

// For performance reasons, the SDK is designed to be purely asynchronous.
// The return value is a future object. If you want to immediately obtain
// the result, you can call `future.get()`.
CompletableFuture<Result<WriteOk, Err>> future = greptimeDB.write(cpuMetric, memMetric);

Result<WriteOk, Err> result = future.get();

if (result.isOk()) {
    LOG.info("Write result: {}", result.getOk());
} else {
    LOG.error("Failed to write: {}", result.getErr());
}

Streaming insert

Streaming insert is useful when you want to insert a large amount of data such as importing historical data.

StreamWriter<Table, WriteOk> writer = greptimeDB.streamWriter();

// write data into stream
writer.write(cpuMetric);
writer.write(memMetric);

// You can perform operations on the stream, such as deleting the first 5 rows.
writer.write(cpuMetric.subRange(0, 5), WriteOp.Delete);

Close the stream writing after all data has been written. In general, you do not need to close the stream writing when continuously writing data.

// complete the stream
CompletableFuture<WriteOk> future = writer.completed();
WriteOk result = future.get();
LOG.info("Write result: {}", result);

High-level API

The high-level API uses an ORM style object to write data to GreptimeDB. It allows you to create, insert, and update data in a more object-oriented way, providing developers with a friendlier experience. However, it is not as efficient as the low-level API. This is because the ORM style object may consume more resources and time when converting the objects.

Create row objects

GreptimeDB Java Ingester SDK allows us to use basic POJO objects for writing. This approach requires the use of Greptime's own annotations, but they are easy to use.

@Metric(name = "cpu_metric")
public class Cpu {
    @Column(name = "host", tag = true, dataType = DataType.String)
    private String host;

    @Column(name = "ts", timestamp = true, dataType = DataType.TimestampMillisecond)
    private long ts;

    @Column(name = "cpu_user", dataType = DataType.Float64)
    private double cpuUser;
    @Column(name = "cpu_sys", dataType = DataType.Float64)
    private double cpuSys;

    // getters and setters
    // ...
}

@Metric(name = "mem_metric")
public class Memory {
    @Column(name = "host", tag = true, dataType = DataType.String)
    private String host;

    @Column(name = "ts", timestamp = true, dataType = DataType.TimestampMillisecond)
    private long ts;

    @Column(name = "mem_usage", dataType = DataType.Float64)
    private double memUsage;
    // getters and setters
    // ...
}

// Add rows
List<Cpu> cpus = new ArrayList<>();
for (int i = 0; i < 10; i++) {
    Cpu c = new Cpu();
    c.setHost("127.0.0." + i);
    c.setTs(System.currentTimeMillis());
    c.setCpuUser(i + 0.1);
    c.setCpuSys(i + 0.12);
    cpus.add(c);
}

List<Memory> memories = new ArrayList<>();
for (int i = 0; i < 10; i++) {
    Memory m = new Memory();
    m.setHost("127.0.0." + i);
    m.setTs(System.currentTimeMillis());
    m.setMemUsage(i + 0.2);
    memories.add(m);
}

Insert data

Write data with POJO objects:

// Saves data

CompletableFuture<Result<WriteOk, Err>> puts = greptimeDB.writeObjects(cpus, memories);

Result<WriteOk, Err> result = puts.get();

if (result.isOk()) {
    LOG.info("Write result: {}", result.getOk());
} else {
    LOG.error("Failed to write: {}", result.getErr());
}

Streaming insert

Streaming insert is useful when you want to insert a large amount of data such as importing historical data.

StreamWriter<List<?>, WriteOk> writer = greptimeDB.objectsStreamWriter();

// write data into stream
writer.write(cpus);
writer.write(memories);

// You can perform operations on the stream, such as deleting the first 5 rows.
writer.write(cpus.subList(0, 5), WriteOp.Delete);

Close the stream writing after all data has been written. In general, you do not need to close the stream writing when continuously writing data.

// complete the stream
CompletableFuture<WriteOk> future = writer.completed();
WriteOk result = future.get();
LOG.info("Write result: {}", result);

Insert data in JSON type

GreptimeDB supports storing complex data structures using JSON type data. With this ingester library, you can insert JSON data using string values. For instance, if you have a table named sensor_readings and wish to add a JSON column named attributes, refer to the following code snippet.

In the low-level API, you can specify the column type as DataType.Json using the addField method to add a JSON column. Then use map to insert JSON data.

// Construct the table schema for sensor_readings
TableSchema sensorReadings = TableSchema.newBuilder("sensor_readings")
        // The code for creating other columns is omitted
        // ...
        // specify the column type as JSON        
        .addField("attributes", DataType.Json)
        .build();

// ...
// Use map to insert JSON data
Map<String, Object> attr = new HashMap<>();
attr.put("location", "factory-1");
sensorReadings.addRow(<other-column-values>... , attr);

// The following code for writing data is omitted
// ...

In the high-level API, you can specify the column type as DataType.Json within the POJO object.

@Metric(name = "sensor_readings")
public class Sensor {
    // The code for creating other columns is omitted
    // ...
    // specify the column type as JSON        
    @Column(name = "attributes", dataType = DataType.Json)
    private Map<String, Object> attributes;
    // ...
}

Sensor sensor = new Sensor();
// ...
// Use map to insert JSON data
Map<String, Object> attr = new HashMap<>();
attr.put("action", "running");
sensor.setAttributes(attr);

// The following code for writing data is omitted
// ...

Debug logs

The ingester SDK provides metrics and logs for debugging. Please refer to Metrics & Display and Magic Tools to learn how to enable or disable the logs.

Ingester library reference

API Documentation

Java

Quick start demos​

Installation​

Connect to database​

Data model​

Low-level API​

Create row objects​

Insert data​

Streaming insert​

High-level API​

Create row objects​

Insert data​

Streaming insert​

Insert data in JSON type​

Debug logs​

Ingester library reference​

Quick start demos

Installation

Connect to database

Data model

Low-level API

Create row objects

Insert data

Streaming insert

High-level API

Create row objects

Insert data

Streaming insert

Insert data in JSON type

Debug logs

Ingester library reference