Asynchronous Kafka Client: kafka_cli

Sample Codes

tutorial-13-kafka_cli.cc

About Compiler

Because of supporting multiple compression methods of Kafka, zlib, snappy, lz4(>=1.7.5), zstd and other third-party libraries are used in the compression algorithms in the Kafka protocol, and they must be installed before the compilation.

It supports both CMake and Bazel for compiling.

CMake: You can use make KAFKA=y to compile a separate library for Kafka protocol(libwfkafka.a和libwfkafka.so) and use cd tutorial; make KAFKA=y to compile kafka_cli.

Bazel: You can use bazel build kafka to compile a separate library for Kafka protocol and use bazel build kafka_cli to compile kafka_cli.

About kafka_cli

Kafka_cli is a kafka client for producing and fetching messages in Kafka.

When you compile the source codes, type the command make KAFKA=y in the tutorial directory or type the command make KAFKA=y tutorial in the root directory of the project.

The program then reads kafka broker server addresses and the current task type (produce/fetch) from the command line:

./kafka_cli <broker_url> [p/c]

The program exists automatically after all the tasks are completed, and all the resources will be completedly freed.

In the command, the broker_url may contain several urls seperated by comma(,).

• For instance, kafka://host:port,kafka://host1:port... or: kafkas://host:port,kafkas://host1:port for kafka over SSL;
• The default port is 9092 for TCP and 9093 for SSL;
• Do not mix 'kafkas://' with "kafka://", otherwise the init function will fail with errno EINVAL;
• If you want to use upstream policy at this layer, please refer to upstream documents.

The following are several Kafka broker_url samples:

kafka://127.0.0.1/

kafka://kafka.host:9090/

kafka://10.160.23.23:9000,10.123.23.23,kafka://kafka.sogou

kafkas://broker1.kafka.sogou,kafkas://broker2.kafka.sogou

Illegal broker_url sample (The first one is SSL, and the second one is not):

kafkas://broker1.kafka.sogou,broker2.kafka.sogou

Principles and Features

Kafka client has no third-party dependencies internally except for the libraries used in the compression. With the high performance of Workflow, When properly configured and in fair environments, tens of thousands of Kafka requests can be processed in one second.

Internally, a Kafka client divides each request into parallel tasks according to the brokers used. In parallel tasks, there is one sub-task for each broker address.

In this way, the efficiency is maximized. Besides, the connection reuse mechanism in the Workflow ensures that the total number of connections is kept within a reasonable range.

If there are multiple topic partitions under one broker address, you may create multiple clients and then create and start separate tasks for each topic partition to increase the throughput.

Creating and Starting Kafka Tasks

To create and start a Kafka task, create a WFKafkaClient first and then call init to initialize that WFKafkaClient.

int init(const std::string& broker_url);

int init(const std::string& broker_url, const std::string& group);

In the above code snippet, broker_url means the address of the kafka broker cluster. Its format is the same as the broker_url in the above section.

group means the group_name of a consumer group, which is used for the consumer group in a fetch task. In the case of produce tasks or fetch tasks without any consumer groups, do not use this interface.

For a consumer group, you can specify the heartbeat interval in milliseconds to keep the heartbeats.

void set_heartbeat_interval(size_t interval_ms);

Then you can create a Kafka task with that WFKafkaClient.

using kafka_callback_t = std::function<void (WFKafkaTask *)>;

WFKafkaTask *create_kafka_task(const std::string& query, int retry_max, kafka_callback_t cb);

WFKafkaTask *create_kafka_task(int retry_max, kafka_callback_t cb);

In the above code snippet, query includes the type of the task, the topic and other properties. retry_max means the maximum number of retries. cb is the user-defined callback function, which will be called after the task is completed.

You can also change the default settings of the task to meet the requirements. For details, refer to KafkaDataTypes.h.

KafkaConfig config;
config.set_client_id("workflow");
task->set_config(std::move(config));

The supported configuration items are described below:

After configuring all the parameters, you can call start interface to start the Kafka task.

About Produce Tasks

1. After you create and initialize a WFKafkaClient, you can specify the topic or other information in the query to create WFKafkaTask tasks.

For example:

int main(int argc, char *argv[])
{
	...
	client = new WFKafkaClient();
	client->init(url);
	task = client->create_kafka_task("api=fetch&topic=xxx&topic=yyy", 3, kafka_callback);

	...
	task->start();
	...
}

2. After the WFKafkaTask is created, call set_key, set_value, add_header_pair and other methods to build a KafkaRecord.

For information about more interfaces on KafkaRecord, refer to KafkaDataTypes.h.

Then you can call add_produce_record to add a KafkaRecord. For the detailed definitions of the interfaces, refer to WFKafkaClient.h.

The second parameter partition in add_produce_record, >=0 means the specified partition; -1 means that the partition is chosen randomly or the user-defined kafka_partitioner_t is used.

For kafka_partitioner_t, you can call the set_partitioner interface to specify the user-defined rules.

For example:

int main(int argc, char *argv[])
{
	...
	WFKafkaClient *client_fetch = new WFKafkaClient();
	client_fetch->init(url);
	task = client_fetch->create_kafka_task("api=produce&topic=xxx&topic=yyy", 3, kafka_callback);
	task->set_partitioner(partitioner);

	KafkaRecord record;
	record.set_key("key1", strlen("key1"));
	record.set_value(buf, sizeof(buf));
	record.add_header_pair("hk1", 3, "hv1", 3);
	task->add_produce_record("workflow_test1", -1, std::move(record));

	...
	task->start();
	...
}

3. You can use one of the four compressions supported by Kafka in the produce task by configuration.

For example:

int main(int argc, char *argv[])
{
	...
	WFKafkaClient *client_fetch = new WFKafkaClient();
	client_fetch->init(url);
	task = client_fetch->create_kafka_task("api=produce&topic=xxx&topic=yyy", 3, kafka_callback);

	KafkaConfig config;
	config.set_compress_type(Kafka_Zstd);
	task->set_config(std::move(config));

	KafkaRecord record;
	record.set_key("key1", strlen("key1"));
	record.set_value(buf, sizeof(buf));
	record.add_header_pair("hk1", 3, "hv1", 3);
	task->add_produce_record("workflow_test1", -1, std::move(record));

	...
	task->start();
	...
}

About Fetch Tasks

You may use consumer group mode or manual mode for fetch tasks.

1. Manual mode

In this mode, you do not need to specify consumer groups, but you must specify topic, partition and offset.

For example:

	client = new WFKafkaClient();
	client->init(url);
	task = client->create_kafka_task("api=fetch", 3, kafka_callback);

	KafkaToppar toppar;
	toppar.set_topic_partition("workflow_test1", 0);
	toppar.set_offset(0);
	task->add_toppar(toppar);

2. Consumer group mode

In this mode, you must specify the name of the consumer group when initializing a client.

For example:

int main(int argc, char *argv[])
{
	...
	WFKafkaClient *client_fetch = new WFKafkaClient();
	client_fetch->init(url, cgroup_name);
	task = client_fetch->create_kafka_task("api=fetch&topic=xxx&topic=yyy", 3, kafka_callback);

	...
	task->start();
	...
}

3. Committing offset

In the consumer group mode, after a message is consumed, you can create a commit task in the callback to automatically submit the consumption record.

For example:

void kafka_callback(WFKafkaTask *task)
{
	...
	commit_task = client.create_kafka_task("api=commit", 3, kafka_callback);

	...
	commit_task->start();
	...
}

Closing the Client

In the consumer group mode, before you close a client, you must call create_leavegroup_task to create a leavegroup_task.

This task will send a leavegroup packet. If no leavegroup_task is started, the group does not know that the client is leaving and will trigger rebalance.

Processing Kafka Results

The data structure of the message result set is KafkaResult, and you can call get_result() in the WFKafkaTask to retrieve the results.

Then you can call the fetch_record in the KafkaResult to retrieve all records of the task. The record is a two-dimensional vector.

The first dimension is topic partition, and the second dimension is the KafkaRecord under that topic partition.

KafkaResult.h contains the definition of KafkaResult.

void kafka_callback(WFKafkaTask *task)
{
	int state = task->get_state();
	int error = task->get_error();

	// handle error states
	...

	protocol::KafkaResult *result = task->get_result();
	result->fetch_records(records);

	for (auto &v : records)
	{
		for (auto &w: v)
		{
			const void *value;
			size_t value_len;
			w->get_value(&value, &value_len);
			printf("produce\ttopic: %s, partition: %d, status: %d, offset: %lld, val_len: %zu\n",
				   w->get_topic(), w->get_partition(), w->get_status(), w->get_offset(), value_len);
		}
	}
	...

	protocol::KafkaResult new_result = std::move(*task->get_result());
	if (new_result.fetch_records(records))
	{
		for (auto &v : records)
		{
			if (v.empty())
				continue;

			for (auto &w: v)
			{
				if (fp)
				{
                	const void *value;
					size_t value_len;
					w->get_value(&value, &value_len);
					fwrite(w->get_value(), w->get_value_len(), 1, fp);
				}
			}
		}
	}
	...
}