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An advanced but simple to use, Kafka producer written in Erlang

Build Status


  • An erlang implementation of a Producer as per 0.8 Kafka wire protocol
  • Produce to a Topic syncronously and asynchronously
  • Option to batch messages and customize the concurrency
  • Several lazy handling of both metadata requests as well as connection pool creation
  • Will automatically buffer when broker goes down and resend, with max downtime size configurable
  • Will automatically start/stop workers based on kafka broker additions/changes/downtime
  • Callbacks to instrument into your monitoring system

ekaf also powers kafboy, the webserver based on Cowboy capable of publishing to ekaf via simple HTTP endpoints that handles more than 100 million events/day as of mid 2014 at Helpshift.

Add a feature request at or check the ekaf web server at

##Features## ###Simple API###

Topic is a binary. and the payload can be a list, a binary, a key-value tuple, or a combination of all the above.

%% Start

%%%% mandatory. ekaf needs atleast 1 broker to connect.
%% To eliminate a SPOF, use an IP to a load balancer to a bunch of brokers
application:set_env(ekaf, ekaf_bootstrap_broker, {"localhost", 9091}),

{ok, _} = application:ensure_all_started(ekaf),

Topic = <<"ekaf">>,

%% Send message to a topic

%% sync
ekaf:produce_sync(Topic, <<"foo">>).

%% async
ok = ekaf:produce_async(Topic, jsx:encode(PropList) ).

%% Send events in batches

%% send many messages in 1 packet by sending a list to represent a payload
{{sent, _OnPartition, _ByPartitionWorker}, _Response }  =
        [<<"foo">>, {<<"key">>, <<"value">>}, <<"back_to_binary">> ]

%% Send events in batches

%% sync
{buffered, _Partition, _BufferSize} =
        [ekaf_utils:itob(X) || X<- lists:seq(1,1000) ]

%% async
{buffered, _Partition, _BufferIndex} =
        [<<"foo">>, {<<"key">>, <<"value">>}, <<"back_to_binary">> ]

%% send entire batch as a list of events
application:set_env(ekaf, ?EKAF_CALLBACK_MASSAGE_BUFFER,

%% route to a partition based on a key or list of tuples ( see below on default, custom logic)
ekaf:produce_async(<<"topic">>, {<<"user1">>,<<"foobar">>}). 
ekaf:produce_sync(<<"topic">>, [{<<"user1">>, <<"foo">>},  {<<"user2">>, <<"bar">>}]).

%% Other helpers that are used internally
%% metadata
%% if you don't want to start workers on app start, make sure to
%%               first get metadata before any produce/publish

%% pick a worker, and directly communicate with it
ekaf:pick(Topic). %synchronous
ekaf:pick(Topic,Callback). %asynchronous

%% see the tests for a complete API, and `ekaf.erl` and `ekaf_lib` for more

See test/ekaf_tests.erl for more


Here's a quick ekaf demo to show broker resiliance, buffering, and instrumenting with callbacks.

on terminal 1

git clone
rebar get-deps compile
cd deps/kafkamocker
rebar compile skip_deps=true && erl -pa ebin -pa deps/*/ebin -s kafkamocker_demo
kafka_consumer_loop INCOMING [<<"1">>,<<"2">>,<<"3">>,<<"4">>,<<"5">>,<<"6">>,

and on terminal 2

cd ekaf
rebar compile skip_deps=true && erl -pa ebin -pa deps/*/ebin -s ekaf_demo
> request 1
> ekaf.ekaf_callback_downtime_saved => 1
> ekaf.mainbroker_unreachable => 1
> ....
> request 20
> ekaf.ekaf_callback_downtime_saved => 1
> ekaf.mainbroker_unreachable => 1
> ekaf.ekaf_callback_time_down => 59280
> ekaf.ekaf_callback_time_to_connect.broker1.0 => 1
> ekaf.ekaf_callback_time_to_connect.broker1.0 => 1
> ekaf.ekaf_callback_downtime_replayed => 1 during ready
> ekaf.ekaf_callback_worker_up.broker1.0 1
> ekaf.ekaf_callback_worker_up.broker1.0 1
> ekaf.ekaf_callback_flush.broker1.0 20
> ekaf.ekaf_callback_flushed_replied.broker1.0.diff 0

To create your own topics and test them see the Kafka Quickstart at

$ bin/ --zookeeper localhost:2181 --replica 1 --partition 1 --topic ekaf

Then change the line in ekaf_demo.erl to point to your kafka server instead of kafkamocker.

Tunable Concurrency

Each worker represents a connection to a broker + topic + partition combination. You can decide how many workers to start for each partition

%% a single topic with 3 partitions will have 15 workers per node
%% all able to batch writes
{ekaf,[{ekaf_per_partition_workers, 5},
       {ekaf_per_partition_workers_max, 10}]}.

You can also set different concurrency strategies for different topics

{ekaf,[{ekaf_per_partition_workers, [
   {<<"topic1">>, 5},              % lazily start a connection pool of 5 workers
   {<<"topic2">>, 1},              % per partition
   {ekaf_per_partition_workers, 1000}    % for remaining topics

By lazily starting a connection pool, these workers only are spawned on receipt of the first produce message received by that worker. More on this below.

Batch writes

You can batch async and sync calls until they reach 1000 for a partition, or 5 seconds of inactivity, which ever comes first.

    {ekaf_max_buffer_size, 100},      %% default
    {ekaf_buffer_ttl     , 5000}      %% default

You can also set different batch sizes for different topics.

{ekaf,[{ekaf_max_buffer_size, [
   {<<"topic1">>, 500},            % topic specific
   {<<"topic2">>, 10000},
   {ekaf_max_buffer_size, 1000}    % for remaining topics

You can also change the default buffer flush on inactivity from 1 second. Topic specific is again possible.

    {ekaf_partition_strategy, 1000}  % if a buffer exists after 1 sec on inactivity, send them

Partition choosing strategy

random ( Faster, since order not maintained among partition workers )


Will deliver to kafka ordered within the same partition worker, but produced messages can go to any random partition worker. As a result, ordering finally at the kafka broker cannot be ensured.



Will attempt to deliver to kafka in the same order than was published by sharding 1000 writes at a time to the same partition worker before switching to another worker. The same partition worker order need not be chosen when run over long durations, but whichever partition worker is picked, its writes will be in order.


Every publish will go to a different partition worker. Helps for in-frequent events that must necessarily not go to the same consumer quickly.

eg: if you want every messages to go to a different partition, you may need 1 worker, and use strict_round_robin

eg: if you have 100 workers per partition and have chosen strict_round_robin, the first 100 events will go to partition1, the next 100 to partition 2, etc


If this strategy has been decided (can be configured for all topics, or for specific topics) then all messages of a tuple form {Key,Bin} will be passed to a function to decide the partition based on Key

ekaf:produce_async_batched(<<"topic">>, {<<"user1">>,<<"foobar">>}).
ekaf:produce_sync(<<"topic">>, [{<<"user1">>, <<"foo">>},  {<<"user2">>, <<"bar">>}]).
% internally if there are 5 partitions then `erlang:phash2(<<"user1">>) rem 5` is done
% to choose a partition. within the partition, workers are again 
% round robin'd even when publishing to the same Key. But you can over-ride the
% default ekaf_callbacks:default_custom_partition_picker/3 implementation

NOTE: You can configure the same strategy for all topics, or pick different for different topics

{ekaf_partition_strategy, [
 {<<"heavy_job">>, strict_round_robin},
 {<<"other_event">>, sticky_round_robin},
 {<<"user_actions">>, custom}, %route to partition based on message key
 {ekaf_partition_strategy,  random}      %% default

No need for Zookeeper

Does not need a connection to Zookeeper for broker info, etc. This adopts the pattern encouraged from the 0.8 protocol

No linked drivers, No NIF's, Minimal Deps.

Deals with the protcol completely in erlang. Pattern matching FTW, see the blogpost that inspired this project ( also see ). Uses gproc for process registry.

Option to re-use worker pool as statsd worker pool to push metrics

In production having when 100's of workers pushing to your favorite statsd client, you may find your statsd client becomeing a bottleneck. Enabling the ?EKAF_PUSH_TO_STATSD_ENABLED at a global or topic level, allows each worker to maintain reference to a UDP socket, so that pushing metrics is naturally load balanced.Enabling this option does not begin sending metrics automatically. Your callback needs to do so like shown below.

% Set the ekaf app options before ekaf starts (or in your config file)
% to enable the push to statsd option (since 1.5.4), register your callback
application:set_env(ekaf, ?EKAF_PUSH_TO_STATSD_ENABLED, true),
application:set_env(ekaf, ?EKAF_CALLBACK_FLUSH_ATOM,  {ekaf_demo, demo_callback}),

% Then to get the metric => N do

demo_callback(Event, _From, _StateName, 
            #ekaf_fsm{ topic = Topic, partition = PartitionId, last_known_size = BufferLength, leader = Leader} = _State,

    Stat = <<Topic/binary,".",  Event/binary, ".broker", (ekaf_utils:itob(Leader))/binary, ".", (ekaf_utils:itob(PartitionId))/binary>>,
    case Event of

Optimized for using on a cluster

  • Works well if embedding into other OTP/rebar style apps ( eg: tested with kafboy)
  • Only gets metadata for the topic being published. ekaf does not start workers until a produce is called, hence easily horizontally scalable - can be added to a load balancer without worrying about creating holding up valuable connections to a broker on bootup. Queries metadata directly from a {ekaf_bootstrap_broker,Broker} during the first produce to a topic, and then caches this data for that topic.
  • Extensive use of records for O(1) lookup
  • By using binary as the preferred format for Topic, etc, - lists are avoided in all places except the {BrokerHost,_Port}.
  • All pg2 and gproc names are prefixed with <<"ekaf.",Topic/binary>> for better namespacing

Concurrency when publishing to multiple topics

Each Topic, will have a pg2 process group, but maintaining the pool is done internally for maintaining round-robin, etc. You can pick a random partition worker for a topic via

ekaf:pick(<<"topic_foo">>, fun(Worker) ->
    case Worker of
        {error, try_again}->
            %% need to get some metadata first for this topic
            %% see how `ekaf:produce_sync/2` does this
        SomeWorkerPid ->
            %% gen_fsm:sync_send_event(SomeWorker, pool_name)
            %% SomeWorker ! info

%% pick/1 and pick/2 also exist for synchronously choosing a worker
%% picking a worker based on the data is a new functionality added in 1.6.0

Fault tolerant

Features you gain by using ekaf for pushing to kafka:

  • Handles changes in kafka metadata auto-magically. Metadata is queried on connection losses and at regular intervals
  • If one partition/broker goes down, messages will go to other available partitions/brokers (as configured on kafka)
  • If all brokers/partitions are unavailable, will start buffering messages in-memory, and replay them when the broker is back. See ekaf_max_downtime_buffer_size for configuring this options. By default this is not set, since kafka restarts are usually quick, and possibility of all brokers down is low.
  • All these network conditions are simulated in the tests. You're covered!


Current callbacks include when the buffer is flushed.

    {ekaf_callback_flush, {mystats, callback_flush}}
    %% where the callback is fun mystats:callback_flush/5

%% mystats.erl
demo_callback(Event, _From, _StateName,
	#ekaf_fsm{ topic = Topic, broker = _Broker, partition = PartitionId, last_known_size = BufferLength, cor_id = CorId, leader = Leader},
Stat = <<Topic/binary,".",  Event/binary, ".broker", (ekaf_utils:itob(Leader))/binary, ".", (ekaf_utils:itob(PartitionId))/binary>>,
case Event of
	io:format("~n ~p flush broker~w#~p when size was ~p corid ~p via:~p",
                      [Topic, Leader, PartitionId, BufferLength, CorId, _From]);

The first argument being binary, can easily be pushed into statsite/statsd/graphite/grafana

State Machines

Each worker is a finite state machine powered by OTP's gen_fsm as opposed to gen_server which is more of a client-server model. Which makes it easy to handle connections breaking, and adding more features in the future. In fact every new topic spawns a worker that first starts in a bootstrapping state until metadata is retrieved. This is a blocking call.

Choosing a worker is done by a worker of ekaf_server for every topic. It looks at the strategy and decides how often to choose a worker and is used internally by ekaf:pick

An example ekaf config


    % required.
    {ekaf_bootstrap_broker, {"localhost", 9091} },
    % pass the {BrokerHost,Port} of atleast one permanent broker. Ideally should be
    %       the IP of a load balancer so that any broker can be contacted

    % optional.
    {ekaf_bootstrap_topics, [ <<"topic">> ]},
    % will start workers for this topic when ekaf starts
    % a lazy and more recommended approach is to ignore this config

    % optional
    % how big is the connection pool per partition
    % eg: if the topic has 3 partitions, then with this eg: 300 workers will be started

    % optional
    {ekaf_max_buffer_size, [{<<"topic">>,10000},                % for specific topic
                            {ekaf_max_buffer_size,100}]},       % for other topics
    % how many events should the worker wait for before flushing to kafka as a batch

    % optional
    {ekaf_partition_strategy, random},
    % if you are not bothered about the order, use random for speed
    % else the default is random

    % optional
    {ekaf_callback_flush, {mystats,callback_flush}},
    % can be used for instrumentating how how batches are sent & hygeine

    % optional
    {ekaf_callback_custom_partition_picker, {ekaf_callbacks, 
    % to always route messages with keys to the same partition


Powering kafboy, a HTTP gateway to ekaf

kafboy ( ) sits on top of ekaf and is powered by Cowboy to send JSON posted to it directly to kafka

POST /async/topic_name
POST /batch/async/topic
POST /safe/batch/async/topic


Running the test on a 2GB RAM vagrant VM, where the broker was local

Test_Async_Multi_Batched = fun(N) ->Seq = lists:seq(1,N), N1 = now(),  ekaf:produce_async_batched(<<"ekaf">>, [ ekaf_utils:itob(X) || X <- Seq]), N2 = now(), N/(timer:now_diff(N2,N1)/1000000) end.

(node@> Test_Async_Multi_Batched(1000).
(node@> Test_Async_Multi_Batched(10000).
(node@> Test_Async_Multi_Batched(100000).

test multiple calls to the async batch

Test_Async_Batched = fun(N) ->Seq = lists:seq(1,N), N1 = now(), [ ekaf:produce_async_batched(<<"ekaf">>, ekaf_utils:itob(X)) || X <- Seq], N2 = now(), N/(timer:now_diff(N2,N1)/1000000) end

(node@> Test_Async_Batched(1000).
(node@> Test_Async_Batched(10000).
(node@> Test_Async_Batched(100000).

test multiple calls to the async without batch

Test_Async = fun(N) -> Seq = lists:seq(1,N), N1 = now(), [ ekaf:produce_async(<<"ekaf">>, ekaf_utils:itob(X)) || X <- Seq], N2 = now(), N/(timer:now_diff(N2,N1)/1000000) end.
(node@>     Test_Async = fun(N) -> Seq = lists:seq(1,N), N1 = now(), [ ekaf:produce_async(<<"ekaf">>, ekaf_utils:itob(X)) || X <- Seq], N2 = now(), N/(timer:now_diff(N2,N1)/1000000) end.
(node@> Test_Async(1000).
(node@> Test_Async(10000).
(node@> Test_Async(100000).


ekaf comes embedded with kafkamocker - an erlang/otp app that any app can embed to simulate a kafka broker. This means that an actual consumer has verified the receipt of the published messages in our eunit tests.

ekaf works well with rebar.

$ rebar get-deps clean compile eunit

==> ekaf (eunit)
test/ekaf_tests.erl:87:<0.174.0>: t_reading_topic_specific_envs ( ) = ok
test/ekaf_tests.erl:89:<0.181.0>: t_pick_from_new_pool ( ) = ok
test/ekaf_tests.erl:91:<0.195.0>: t_request_metadata ( ) = ok
test/ekaf_tests.erl:93:<0.199.0>: t_request_worker_state ( ) = ok
test/ekaf_tests.erl:96:<0.203.0>: t_produce_sync_to_topic ( ) = ok
test/ekaf_tests.erl:98:<0.209.0>: t_produce_sync_multi_to_topic ( ) = ok
test/ekaf_tests.erl:100:<0.215.0>: t_produce_sync_in_batch_to_topic ( ) = ok
test/ekaf_tests.erl:102:<0.222.0>: t_produce_sync_multi_in_batch_to_topic ( ) = ok
test/ekaf_tests.erl:105:<0.229.0>: t_produce_async_to_topic ( ) = ok
test/ekaf_tests.erl:107:<0.236.0>: t_produce_async_multi_to_topic ( ) = ok
test/ekaf_tests.erl:109:<0.243.0>: t_produce_async_in_batch_to_topic ( ) = ok
test/ekaf_tests.erl:111:<0.251.0>: t_produce_async_multi_in_batch_to_topic ( ) = ok
test/ekaf_tests.erl:114:<0.259.0>: t_max_messages_to_save_during_kafka_downtime ( ) = ok
test/ekaf_tests.erl:116:<0.277.0>: t_restart_kafka_broker ( ) = ok
test/ekaf_tests.erl:118:<0.290.0>: t_change_kafka_config ( ) = ok
test/ekaf_tests.erl:120:<0.324.0>: t_massage_buffer_encode_messages_as_one_large_message ( ) = ok
All 32 tests passed.
Cover analysis: /Users/bosky/testbed/ekaf/.eunit/index.html

Code Coverage:
ekaf                   : 51%
ekaf_callbacks         : 87%
ekaf_demo              :  0%
ekaf_fsm               : 53%
ekaf_lib               : 62%
ekaf_picker            : 68%
ekaf_protocol          : 70%
ekaf_protocol_metadata : 78%
ekaf_protocol_produce  : 68%
ekaf_server            : 43%
ekaf_server_lib        : 64%
ekaf_socket            : 57%
ekaf_sup               : 33%
ekaf_utils             : 14%

Total                  : 50%


Copyright 2015, Helpshift, Inc.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
See the License for the specific language governing permissions and
limitations under the License.

Goals for v2.0.0


A minimal, high-performance Kafka client in Erlang.




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