chore: docs

README.md

@@ -83,10 +83,21 @@ Checkout the `Klife.Client` [docs](https://hexdocs.pm/klife/Klife.Client.html) f
 
 ## Producer performance
 
+I've test it against the 3 awesome community kafka libraries [brod](https://github.com/kafka4beam/brod)
+and [kafka_ex](https://github.com/kafkaex/kafka_ex) and [erlkaf](https://github.com/silviucpp/erlkaf)
+which are the most popular ones.
+
+The relevant client configuration should be equal on all clients and they are:
+
+- required_acks: all
+- max_inflight_request: 1
+- linger_ms: 0
+- max_batch_size: 512kb
+
 ### Produce sync
 
-In order to test sync produce the performance I've prepared a benchmark that uses [benchee](https://github.com/bencheeorg/benchee) to produce
-kafka records on kafka cluster running locally on my machine.
+In order to test sync produce performance we prepared a benchmark that uses [benchee](https://github.com/bencheeorg/benchee) to produce
+kafka records on kafka cluster running locally.
 
 The details can be checked out on `benchmark.ex` mix task and the results on `bechmark_results`.
 
@@ -100,29 +111,22 @@ mix benchmark producer_sync 16
 Each iteration of the benchmark produces 3 records for 3 different topics in paralel and wait for the completion
 in order to move to the next iteration.
 
-I've test it against the 2 awesome community kafka libraries [brod](https://github.com/kafka4beam/brod)
-and [kafka_ex](https://github.com/kafkaex/kafka_ex) which are the most popular ones.
-
 The main point driving the Klife's performance is the batching efficiency. As far as I can tell:
 
 - Klife: Batches everything that can be batched together in a single TCP request
 - Brod: Batches records only for the same topic/partition in a single TCP request
 - Kafka_ex: Does not batch records (I'm not sure if there is a way to change this behaviour)
 
-The relevant client configuration are equal on all clients and they are:
-
-- required_acks: all
-- max_inflight_request: 1
-- linger_ms: 0
-- max_batch_size: 512kb
-
 With this scenario I've executed the benchmark increasing the `parallel` attribute from
 benchee from 1 to 16, doubling it each round. The results are the following:
 
 ![](./assets/producer_sync_benchmark.png "Producer Sync Benchmark Results")
 
 ### Produce async
 
+In order to test async produce performance we prepared a test script that produces records asynchronously on
+a kafka cluster running locally.
+
 The asynchronous benchmark spawns N parallel processes producing to one of 3 topics in a loop. After 10 seconds,
 it calculates the difference between the initial and current offsets for each topic partition to determine
 the total records produced and the throughput (records per second).