Thursday, September 5, 2019

Multi-Nodes Redis Cluster With Docker

Read this article on my new blog

As part of my on-boarding/training at RedisLabs I continue to play with the product, and I have decided today to install a local 3 nodes cluster of Redis Enterprise Server (RS); and show how easy is to move from a single node/shard database to a multi nodes highly available one.
Once your cluster is up & running, you will kill some containers to see how the system automatically fail-over to guarantee service continuity.
The deployment will look more or less like the schema below, (coming from RedisLabs documentation)

Go to the complete article here.

Tuesday, September 3, 2019

Getting Started With Redis Streams & Java

Read this article on my new blog

As you may have seen, I have joined Redis Labs a month ago; one of the first task as a new hire is to learn more about Redis. So I learned, and I am still learning.
This is when I discovered Redis Streams. I am a big fan of streaming-based applications so it is natural that I start with a small blog post explaining how to use Redis Streams and Java.

Tuesday, August 8, 2017

Getting started with MapR-DB Table Replication


MapR-DB Table Replication allows data to be replicated to another table that could be on on the same cluster or in another cluster. This is different from the automatic and intra-cluster replication that copies the data into different physical nodes for high availability and prevent data loss.

This tutorial focuses on the MapR-DB Table Replication that replicates data between tables on different clusters.

Replicating data between different clusters allows you to:

  • provide another level of disaster recovery that protects your data and applications against global data center failure,
  • push data close to the applications and users,
  • aggregate the data from mutliple datacenters.

Replication Topologies

MapR-DB Table Replication provides various topologies to adapt the replication to the business and technical requirements:

  • Master-slave replication : in this topology, you replicate one way from source tables to replicas. The replicas can be in a remote cluster or in the cluster where the source tables are located.
  • Multi-Master replication : in this replication topology, there are two master-slave relationships, with each table playing both the role of a master and a slave. Client applications update both tables and each table replicates updates to the other.

In this example you will learn how to setup multi-master replication.

Friday, January 20, 2017

Getting Started With Kafka REST Proxy for MapR Streams


MapR Ecosystem Package 2.0 (MEP) is coming with some new features related to MapR Streams:

MapR Ecosystem Packs (MEPs) are a way to deliver ecosystem upgrades decoupled from core upgrades - allowing you to upgrade your tooling independently of your Converged Data Platform. You can lean more about MEP 2.0 in this article.

In this blog we describe how to use the REST Proxy to publish and consume messages to/from MapR Streams. The REST Proxy is a great addition to the MapR Converged Data Platform allowing any programming language to use MapR Streams.

The Kafka REST Proxy provided with the MapR Streams tools, can be used with MapR Streams (default), but also used in a hybrid mode with Apache Kafka. In this article we will focus on MapR Streams. <!-- more -->


  • MapR Converged Data Platform 5.2 with MEP 2.0
    • with MapR Streams Tools
  • curl, wget or any HTTP/REST Client tool

Create the MapR Streams and Topic

A stream is a collection of topics that you can manage as a group by:

  1. Setting security policies that apply to all topics in that stream
  2. Setting a default number of partitions for each new topic that is created in the stream
  3. Set a time-to-live for messages in every topic in the stream

You can find more information about MapR Streams concepts in the documentation.

On your Mapr Cluster or Sandbox, run the following commands:

$ maprcli stream create -path /apps/iot-stream -produceperm p -consumeperm p -topicperm p

$ maprcli stream topic create -path /apps/iot-stream -topic sensor-json -partitions 3

$ maprcli stream topic create -path /apps/iot-stream -topic sensor-binary -partitions 3

Start Kafka Console Producers and Consumers

Open two terminal windows and run the consumer Kafka utilities using the following commands:


  • Topic sensor-json
$ /opt/mapr/kafka/kafka-0.9.0/bin/ --new-consumer --bootstrap-server --topic /apps/iot-stream:sensor-json
  • Topic sensor-binary
$ /opt/mapr/kafka/kafka-0.9.0/bin/ --new-consumer --bootstrap-server --topic /apps/iot-stream:sensor-binary

This two terminal windows will allow you to see the messages posted on the different topics

Using Kafka REST Proxy

Inspect Topic Metadata

The endpoint /topics/[topic_name] allows you to get some informations about the topic. In MapR Streams, topics are part of a stream identified by a path; to use the topic using the REST API you have to use the full path, and encode it in the URL; for example:

  • /apps/iot-stream:sensor-json will be encoded with %2Fapps%2Fiot-stream%3Asensor-json

Run the following command, to get information about the sensor-json topic

$ curl -X GET  http://localhost:8082/topics/%2Fapps%2Fiot-stream%3Asensor-json

Note: For simplicity reason I am running the command from the node where the Kafka REST proxy is running, so it is possible to use localhost.

You can print JSON in a pretty way, by adding a Python command such as :

$ curl -X GET  http://localhost:8082/topics/%2Fapps%2Fiot-stream%3Asensor-json | python -m json.tool

Default Stream

As mentioned above, the Stream path is part of the topic name you have to use in the command; however it is possible to configure the MapR Kafka REST Proxy to use a default stream. For this you should add the following property in the /opt/mapr/kafka-rest/kafka-rest-2.0.1/config/ file:


    When you change the Kafka REST proxy configuration, you must restart the service using maprcli or MCS.

    The main reason to use the properties is to simplify the URLs used by the application for example

    • with you can use curl -X GET http://localhost:8082/topics/
    • without this configuration, or if you want to use a specific stream you must specify it in the URL http://localhost:8082/topics/%2Fapps%2Fiot-stream%3Asensor-json

    In this article, all the URLs contains the encoded stream name, like that you can start using the Kafka REST proxy without changind the configuration and also use it with different streams.

Publishing Messages

The Kafka REST Proxy for MapR Streams allows application to publish messages to MapR Streams. Messages could be send as JSON or Binary content (base64 encoding).

To send a JSON Message:

  • the query should be a HTTP POST
  • the Content-Type should be : application/vnd.kafka.json.v1+json
  • the Body:
        "temp" : 10 ,
        "speed" : 40 ,
        "direction" : "NW"

The complete request is:

curl -X POST -H "Content-Type: application/vnd.kafka.json.v1+json" \
  --data '{"records":[{"value": {"temp" : 10 , "speed" : 40 , "direction" : "NW"}  }]}' \

You should see the message printed in the terminal window where the /apps/iot-stream:sensor-json consumer is running.

To send a binary Message:

  • the query should be a HTTP POST
  • the Content-Type should be : application/vnd.kafka.binary.v1+json
  • the Body:

Note that SGVsbG8gV29ybGQ= is the string "Hello World" encoded in Base64.

The complete request is:

curl -X POST -H "Content-Type: application/vnd.kafka.binary.v1+json" \
  --data '{"records":[{"value":"SGVsbG8gV29ybGQ="}]}' \

You should see the message printed in the terminal window where the /apps/iot-stream:sensor-binary consumer is running.

Sending multiple messages

The records field of the HTTP Body allows you to send multiple messages for example you can send:

curl -X POST -H "Content-Type: application/vnd.kafka.json.v1+json" \
  --data '{"records":[{"value": {"temp" : 12 , "speed" : 42 , "direction" : "NW"}  }, {"value": {"temp" : 10 , "speed" : 37 , "direction" : "N"}  } ]}' \

This command will send 2 messages, and increment the offset by 2. You can do the same with binary content, just add new element in the JSON array; for example:

curl -X POST -H "Content-Type: application/vnd.kafka.binary.v1+json" \
  --data '{"records":[{"value":"SGVsbG8gV29ybGQ="}, {"value":"Qm9uam91cg=="}]}' \

As you probably know, it is possible to set a key to a message to be sure that all the messages with the same key will arrive in the same partition. For this, add the key attribute to the message as follow:

      "key": "K001",
        "temp" : 10 ,
        "speed" : 40 ,
        "direction" : "NW"

Now that you know how to post messages to MapR Stream topics usinf the REST Proxy, let's see how to consume the messages.

Consuming Messages

The REST proxy can also be used to consume messages from topics; for this you need to:

  1. Create a consumer instance.
  2. Use this URL returned by the first call to read message.
  3. Delete the consumer instanced if needed.

Creating the consumer instance

The following request creates the consumer instance:

curl -X POST -H "Content-Type: application/vnd.kafka.v1+json" \
      --data '{"name": "iot_json_consumer", "format": "json", "auto.offset.reset": "earliest"}' \

The response from the server looks like:


Note that we have used the /consumers/[topic_name] to create the consumer.

The base_uri will be used by the subsequent requests to get the messages from the topic. Like any MapR Streams/Kafka consumer the auto.offset.reset defines its behavior. In this example the value is set to earliest, this means that the consumer will read the messages from the beginning. You can find more information about the consumer configuration in the MapR Streams documentation.

Consuming the messages

To consume the messages, just add the Mapr Streams topic to the URL of the consumer isntance.

The following request consumes the messages from the topic:

curl -X GET -H "Accept: application/vnd.kafka.json.v1+json" \

This call returns the messages in a JSON document:


Each call to the API returns the new messages published, based on the offset of the last call.

Note that the Consumer will be destroyed:

  • after some idle time set by the (default value set to 300000ms / 5 minutes)
  • where it is destroyed using a REST API call (see below).

Consuming binary format messages

The approach is the same if you need to consume binary messages, you need to change the format and accept header.

Call this URL to create a consumer instance for the binary topic:

curl -X POST -H "Content-Type: application/vnd.kafka.v1+json" \
      --data '{"name": "iot_binary_consumer", "format": "binary", "auto.offset.reset": "earliest"}' \

Then consume messages, the accept header is set to application/vnd.kafka.binary.v1+json:

curl -X GET -H "Accept: application/vnd.kafka.binary.v1+json" \

This call returns the messages in a JSON document, and the value is encoded in Base64


Delete consumer instances

As mentioned before the consumer will be destroyed automatically based on the configuration of the REST Proxy; it is also possible to destroyed the instance using the consumer instance URI and an HTTP DELETE call, as follow:

curl -X DELETE http://localhost:8082/consumers/%2Fapps%2Fiot-stream%3Asensor-binary/instances/iot_binary_consumer


In this article you have learned how to use the Kafka REST Proxy for MapR Streams that allow any application to use messages published in the MapR Converged Data Platform.

You can find more information about the Kafka REST Proxy in the MapR documentation and the following resources:

Wednesday, January 4, 2017

Getting Started with MQTT and Java

MQTT (MQ Telemetry Transport) is a lightweight publish/subscribe messaging protocol. MQTT is used a lot in the Internet of Things applications, since it has been designed to run on remote locations with system with small footprint.

The MQTT 3.1 is an OASIS standard, and you can find all the information at

This article will guide you into the various steps to run your first MQTT application:

  1. Install and Start a MQTT Broker
  2. Write an application that publishes messages
  3. Write an application that consumes messages

The source code of the sample application is available on GitHub.

Tuesday, October 11, 2016

Getting started with Apache Flink and Kafka


Apache Flink is an open source platform for distributed stream and batch data processing. Flink is a streaming data flow engine with several APIs to create data streams oriented application.

It is very common for Flink applications to use Apache Kafka for data input and output. This article will guide you into the steps to use Apache Flink with Kafka.

Monday, September 26, 2016

Streaming Analytics in a Digitally Industrialized World

Get an introduction to streaming analytics, which allows you real-time insight from captured events and big data. There are applications across industries, from finance to wine making, though there are two primary challenges to be addressed.

Did you know that a plane flying from Texas to London can generate 30 million data points per flight? As Jim Daily of GE Aviation notes, that equals 10 billion data points in one year. And we’re talking about one plane alone. So you can understand why another top GE executive recently told Ericsson Business Review that "Cloud is the future of IT," with a focus on supporting challenging applications in industries such as aviation and energy.