Category Archives: Avro

Robust Message Serialization in Apache Kafka Using Apache Avro, Part 3

Categories: Avro CDH How-to Kafka

Part 3: Configuring Clients

Earlier, we introduced Kafka Serializers and Deserializers that are capable of writing and reading Kafka records in Avro format. In this part we will going to see how to configure producers and consumers to use them.

Setting up a Kafka Topic for use as a Schema Store

KafkaTopicSchemaProvider works with a Kafka topic as its persistent store. This topic will contain at most thousands of records: the schemas. It does not need multiple partitions,

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Robust Message Serialization in Apache Kafka Using Apache Avro, Part 2

Categories: Avro CDH How-to Kafka

Implementing a Schema Store

In Part 1, we saw the need for an Apache Avro schema provider but did not implement one. In this part we will implement a schema provider that works with Apache Kafka as storage.

In-Memory SchemaStore

First we can implement an in-memory store for schemas. This is useful to understand the requirements for such a store and as the cache of the Kafka backed store. A SchemaStore has to be quick in looking up VersionedSchema entries.

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Robust Message Serialization in Apache Kafka Using Apache Avro, Part 1

Categories: Avro CDH How-to Kafka

In Apache Kafka, Java applications called producers write structured messages to a Kafka cluster (made up of brokers). Similarly, Java applications called consumers read these messages from the same cluster.  In some organizations, there are different groups in charge of writing and managing the producers and consumers. In such cases, one major pain point can be in the coordination of the agreed upon message format between producers and consumers.

This example demonstrates how to use Apache Avro to serialize records that are produced to Apache Kafka while allowing evolution of schemas and nonsynchronous update of producer and consumer applications.

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Performance comparison of different file formats and storage engines in the Apache Hadoop ecosystem

Categories: Avro Guest Hadoop HBase Kudu Parquet

Zbigniew Baranowski is a database systems specialist and a member of a group which provides and supports central database and Hadoop-based services at CERN. This blog was originally released on CERN’s “Databases at CERN” blog, and is syndicated here with CERN’s permission.



This post presents a performance comparison of few popular data formats and storage engines available in the Apache Hadoop ecosystem: Apache Avro,

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How-to: Improve Apache HBase Performance via Data Serialization with Apache Avro

Categories: Avro HBase Performance

Taking a thoughtful approach to data serialization can achieve significant performance improvements for HBase deployments.

The question of using tall versus wide tables in Apache HBase is a commonly discussed design pattern (see reference here and here). However, there are more considerations here than making that simple choice. Because HBase stores each column of a table as an independent row in the underlying HFiles, significant storage overhead can occur when storing small pieces of information.

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