Client configuration for Bufstream


This product is in beta. For more information about using Bufstream in production, contact us.

Bufstream is API-compatible with Apache Kafka and therefore works out of the box with any client. The examples below use franz-go and the Apache Kafka Java Client to illustrate the recommended settings.

Connecting to Bufstream

To connect clients to Bufstream, provide Bufstream's bootstrap URL and port to the client. Bufstream uses the standard Kafka port 9092 by default, but this value is configurable.

Minimizing inter-zone network traffic

Bufstream uses flags within the client ID to determine which availability zone (AZ) it's operating in and respond to service discovery requests with only the addresses of zone-local agents. To configure your clients for zone awareness, append a zone argument to the client ID. For example, use a client ID of my-app-001,zone=us-east-1a for the first instance of an application called my-app in AWS us-east-1a. On deploy, Bufstream will emit the AZ values that you can use in client configuration.

Following standard Kafka practice, take care to make client IDs unique.

Optimizing performance and write throughput

Because Bufstream agents write directly to object storage and don't have a local disk, they're much more latency-sensitive. Therefore, Bufstream's optimal performance occurs when processing workloads with large batch sizes or high concurrency. To achieve high write throughput and optimal performance in Bufstream, we recommend configuring the following settings:

  • Linger: Linger determines how long a producer waits for more records before triggering a request, and is the most important client setting. For most Bufstream deployments, a default linger value of 100 ms is appropriate. The ideal value should be about equal to the 50th percentile of the record produce delay, or half of the writer flush interval.

  • Batch Size: Batch size determines how a producer buffers before writing. The maximum batch size shouldn't exceed the maximum flush size (15 MB). A good batch size target to start with is 1 MB. Most Bufstream deployments should perform optimally with a batch size of 1-4 MB.

  • Max Request Size: We recommend using the client default, as this is effectively controlled by the configured batch size.

Configuring both batch size and linger helps clients optimize for Bufstream's latency sensitivity and reduce data loss.

Example Configuration

The examples below illustrate how to configure franz-go and the Apache Kafka Java Client using the recommended settings for performance, throughput, and zone awareness.


package main
import (


func NewKafkaClient(bootstrapServers []string, groupID, topic string) (*kgo.Client, error) {
	return kgo.NewClient(
        // Connects your clients to Bufstream's bootstrap servers
        // Sets the client id to the name of your bufstream app comma separated from the availability zone.
        // this field is whitespace dependent. Do not add spaces around the comma or equal sign.
        // Sets the upper bound of the batch size to be sent. In Bufstream, this shouldn't exceed the max flush rate of 15 MB.
        // Sets how long a producer will wait for records before batching them into a request.

Kafka Java Client

package com.mycompany.bufstream.client;

import org.apache.kafka.clients.CommonClientConfigs;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;

import java.util.Properties;

public class MyClient {

    private final KafkaProducer<String, String> producer;
    private final KafkaConsumer<String, String> consumer;

    public MyClient() {
        Properties producerProperties = new Properties();
        producerProperties.put(CommonClientConfigs.BOOTSTRAP_SERVERS_CONFIG, "bufstream:9092");
        // Set producer client id
        producerProperties.put(CommonClientConfigs.CLIENT_ID_CONFIG, "my-bufstream-001,zone=a");
        producerProperties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
        producerProperties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");

        // Set producer "linger.ms"
        producerProperties.put(ProducerConfig.LINGER_MS_CONFIG, "100"); // 100 ms
        // Set producer batch size
        producerProperties.put(ProducerConfig.BATCH_SIZE_CONFIG, "15728640"); // 15*1024*1024
        this.producer = new KafkaProducer<>(producerProperties);

        Properties consumerProperties = new Properties();
        consumerProperties.put(CommonClientConfigs.BOOTSTRAP_SERVERS_CONFIG, "bufstream:9092");
        consumerProperties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");
        consumerProperties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");
        consumerProperties.put(CommonClientConfigs.GROUP_ID_CONFIG, "my-group");

        // Set consumer "fetch.wait.max.ms"
        consumerProperties.put(ConsumerConfig.FETCH_MAX_WAIT_MS_CONFIG, "1000"); // 1 second
        this.consumer = new KafkaConsumer<>(consumerProperties);

To see how we configured our clients in Benchmark runs, check out the benchmarks and cost documentation. For more information on configuring Bufstream, consult the configuration docs for AWS and GCP.