Log Aggregation With Kafka And Redis

Asaf Yigal has a two-part series on comparing Apache Kafka and Redis for moving log events into Elasticsearch.  Part 1 explains the technologies:

Redis is a bit different from Kafka in terms of its storage and various functionalities. At its core, Redis is an in-memory data store that can be used as a high-performance database, a cache, and a message broker. It is perfect for real-time data processing.

The various data structures supported by Redis are strings, hashes, lists, sets, and sorted sets. Redis also has various clients written in several languages which can be used to write custom programs for the insertion and retrieval of data. This is an advantage over Kafka since Kafka only has a Java client. The main similarity between the two is that they both provide a messaging service. But for the purpose of log aggregation, we can use Redis’ various data structures to do it more efficiently.

Part 2 compares the two technologies and explains which works better when:

Kafka heavily relies on the machine memory (RAM). As we see in the previous graph, utilizing the memory and storage is an optimal way to maintain a steady throughput. Its performance depends on the data consumption rate. In the case that consumers don’t consume data fast enough, Kafka will have to read from a disk and not from memory which will slow down its performance.

As you might expect, the answer for which technology to use is “it depends.”

Related Posts

Azure Databricks Security

Tristan Robinson looks at what’s currently available in terms of security on Azure Databricks: You’ll notice that as part of this I’m retrieving the secrets/GUIDS I need for the connection from somewhere else – namely the Databricks-backed secrets store. This avoids exposing those secrets in plain text in your notebook – again this would not […]

Read More

Testing Kafka Streams Applications

Yeva Byzek continues her series on testing Kafka-based streaming applications: When you create a stream processing application with Kafka’s Streams API, you create a Topologyeither using the StreamsBuilder DSL or the low-level Processor API. Normally, the topology runs with the KafkaStreams class, which connects to a Kafka cluster and begins processing when you call start(). For testing though, connecting to a running […]

Read More


December 2016
« Nov Jan »