Hello JSON, Hello YAML and Goodbye XML!

March 20, 2019

This is an example showing the use of YAML configuration, and populating a Hazelcast grid with data in JSON format that has been extracted from a traditional relational database.

So the data goes from tables into “NoSQL” format.

Hello YAML, Goodbye XML

Hazelcast has many ways to be configured.

Sensible defaults mean you don’t have to bother with most of the configuration. But you can do the setup from Java or XML. From Hazelcast 3.12 onwards, YAML is also possible.

So to configure Hazelcast from YAML, you might have a file named hazelcast.yml containing this:

  name: 'jsongrid'
   enabled: true
   url: 'http://localhost:8080/hazelcast-mancenter'
    port: 9701
     enabled: false
     enabled: true

This would replace old XML style,


 <management-center enabled="true">http://localhost:8080/hazelcast-mancenter</management-center>

    <multicast enabled="false"/>
    <tcp-ip enabled="true">

Both achieve the same thing; it’s just a question of preference.

Hello JSON

The next thing you want to do is have easier handling of JSON.

Hazelcast has always supported strings as data keys and data values, and we can easily store JSON in this format. JSON is just a string with a loose type system applied. There is no strict format checking, it’s free-form, which is the benefit (or drawback!) of JSON.

However, for efficient querying of JSON, we need Hazelcast to understand that the data is a JSON object.

The Example – jsongrid

This example is a Hazelcast grid holding JSON data, named jsongrid.

The example here consists of four modules. Build in the usual Maven way with

mvn clean install

What this will build is four modules:

  • jsongrid-database An HSQL database
  • jsongrid-database-tester A JDBC routine to prove the database is valid.
  • jsongrid-server A Hazelcast server process, one of at least one in the grid
  • jsongrid-client-java A client of this grid that happens to be written in Java too


The whole motivation here is to see how much can be done without considering Java.

The database is a relational database, typical of many places. Here we code it as HSQL, but other choices such as MySql, Postgres and Oracle should be easy replacements.

The Hazelcast grid reads from this relational database to store the same content in memory as JSON objects. It generically does this, so there is no need to define the data model, the code deduces it.

The Hazelcast client queries data in the Hazelcast grid, running a query against fields in the JSON objects.

So the basic idea here is around JSON. The grid deduces how to get JSON in from an external source (a relational database), and the client queries this data without caring about its origin.

From a developer or architects viewpoint, we can be querying JSON data at memory speed that somehow came from a relational database, without bothering too much about the details.

The Relational Database (RDBMS)

The first module to look at is the relational database itself.

In most organisations there are relational databases.

In this example, we run an HSQL database, but you can substitute MySql, Oracle, Postgres, whatever you
like. Connectivity is via JPA over JDBC, so as long as the database follows this standard, it won’t matter.

Run a database instance using this command:

java -jar jsongrid-database/target/jsongrid-database.jar

This module will take two table definitions (in json-database/src/main/resources/schema.sql) and create the according to tables. Data is also loaded (from json-database/src/main/resources/data.sql).

The volumes here are 45 rows for the POTUS table and 48 rows for the VPOTUS table. So we can easily afford to load everything into memory rather than a selection, and 93 data records will easily fit in one Hazelcast server JVM. We can and should run more servers, but one would do if resources are really constrained.

Test Data

The test data here is a list of the Presidents of the United States and Vice-Presidents of the United States

Each is a row in a table. Some columns may be null, such as a middle name. Other columns may not be null, such as the date of taking office.

Note, other politicians are available 🙂

After a successful build, the database is run with:

java -jar jsongrid-database/target/jsongrid-database.jar

The database is a silent resource. We don’t really know what is in it unless an error message appears, so we need some sort of way to prove it is correctly loaded. This is where the jsongrid-database-tester comes in.

Independent database tester

Just to prove the database is working properly, a database tester is included. This is a standalone routine that connects to the database, does a query and outputs the results. It contains nothing to do with Hazelcast.

Run this as:

java -jar jsongrid-database/target/jsongrid-database.jar

What this does is dump the potus and vpotus tables to the screen. The output should include this:

[1 row]
[1 row]

At the time of writing, there have been 45 presidents and 48 vice-presidents, so that’s how many rows should be found.

The Hazelcast Grid

This needs the database to be running.

Start a Hazelcast grid member using

java -jar jsongrid-server/target/jsongrid-server.jar

One is enough for this data volume 93 records! Start more servers if you like.

The Logic

The key part of the Hazelcast server is the map loader.

The server queries all database tables in the database. It should find POTUS and VPOTUS tables as that’s what we insert into the database.

For each of these, the server will attempt to access a map in Hazelcast with the same name, which will trigger a map loader.

The map loader will find all rows in the table and convert each to JSON to store in Hazelcast.

So effectively every row in every table in the database becomes an entry in a Hazelcast map, in JSON format.

Hazelcast doesn’t need to know the format of the table, it can deduce it from the information returned.

The JSON Client

This needs one or more members of the Hazelcast grid to be running.

The demonstrator of value here is a routine that connects to the Hazelcast grid and queries the data.

In this example, it’s written in Java and run with:

java -jar jsongrid-client-java/target/jsongrid-client-java.jar

If you run it, you should get query output incuding:

IMap 'POTUS', size 45
IMap 'VPOTUS', size 48
- - - - - - - - - - - - - -
{ "ID" : "2", "NAME" : "Thomas Jefferson", "TOOKOFFICE" : "1797-03-04", "LEFTOFFICE" : "1801-03-04" }
{ "ID" : "27", "NAME" : "James S Sherman", "TOOKOFFICE" : "1909-03-04", "LEFTOFFICE" : "1912-10-30" }

The query is expressed as a string, TOOKOFFICE LIKE '%-03-04'.

The results come back as JSON, { "ID" : "2", "NAME" : "Thomas Jefferson", "TOOKOFFICE" : "1797-03-04", "LEFTOFFICE" : "1801-03-04" }.

Java is just a choice. You could do this with GoLang, C#, C++, Node.js, etc. Any of the client languages, whatever suits you best.

Query 1 : John

The query here is looking for John as part of the US president’s name.

Specifically FIRSTNAME = 'John' OR MIDDLENAME1 = 'John' OR MIDDLENAME2 = 'John' OR LASTNAME = 'John'.

This will match against this JSON, amongst others

{ "ID" : "45",
  "FIRSTNAME" : "Donald",
  "MIDDLENAME1" : "John",  
  "MIDDLENAME2" : "null",
  "LASTNAME" : "Trump",
  "TOOKOFFICE" : "2017-01-20",
  "LEFTOFFICE" : "null",
  "AKA" : "null" }

The 45th president is Donald Trump, his first middle name is John.

You should get six matches in total:

{ "ID" : "2", "FIRSTNAME" : "John", "MIDDLENAME1" : "null", "MIDDLENAME2" : "null", "LASTNAME" : "Adams", "TOOKOFFICE" : "1797-03-04", "LEFTOFFICE" : "1801-03-04", "AKA" : "null" }
{ "ID" : "45", "FIRSTNAME" : "Donald", "MIDDLENAME1" : "John", "MIDDLENAME2" : "null", "LASTNAME" : "Trump", "TOOKOFFICE" : "2017-01-20", "LEFTOFFICE" : "null", "AKA" : "null" }
{ "ID" : "6", "FIRSTNAME" : "John", "MIDDLENAME1" : "Quincy", "MIDDLENAME2" : "null", "LASTNAME" : "Adams", "TOOKOFFICE" : "1825-03-04", "LEFTOFFICE" : "1829-03-04", "AKA" : "null" }
{ "ID" : "30", "FIRSTNAME" : "John", "MIDDLENAME1" : "Calvin", "MIDDLENAME2" : "null", "LASTNAME" : "Coolidge", "TOOKOFFICE" : "1923-08-02", "LEFTOFFICE" : "1929-03-04", "AKA" : "Calvin" }
{ "ID" : "10", "FIRSTNAME" : "John", "MIDDLENAME1" : "null", "MIDDLENAME2" : "null", "LASTNAME" : "Tyler", "TOOKOFFICE" : "1841-04-04", "LEFTOFFICE" : "1845-03-04", "AKA" : "null" }
{ "ID" : "35", "FIRSTNAME" : "John", "MIDDLENAME1" : "Fitzgerald", "MIDDLENAME2" : "null", "LASTNAME" : "Kennedy", "TOOKOFFICE" : "1961-01-20", "LEFTOFFICE" : "1963-11-22", "AKA" : "Jack" }

Query 2 : 4th March

Until the ratification of the 12th amendment to the constitution, inauguration day for the president was usually the 4th of March. And, indirectly for the vice president too.

We run this question to see which vice-presidents took office on the 4th March.


This looks for records where the date of taking office (the TOOKOFFICE column) matches the pattern.

There are 29 results, such as

{ "ID" : "2", "NAME" : "Thomas Jefferson", "TOOKOFFICE" : "1797-03-04", "LEFTOFFICE" : "1801-03-04" }
{ "ID" : "27", "NAME" : "James S Sherman", "TOOKOFFICE" : "1909-03-04", "LEFTOFFICE" : "1912-10-30" }
{ "ID" : "14", "NAME" : "John C Breckinridge", "TOOKOFFICE" : "1857-03-04", "LEFTOFFICE" : "1861-03-04" }
{ "ID" : "6", "NAME" : "Daniel D Tompkins", "TOOKOFFICE" : "1817-03-04", "LEFTOFFICE" : "1825-03-04" }
{ "ID" : "19", "NAME" : "William A Wheeler", "TOOKOFFICE" : "1877-03-04", "LEFTOFFICE" : "1881-03-04" }

Note that query results aren’t ordered, so you might get them a different way around. But essentially this shows

{ "ID" : "2",
  "NAME" : "Thomas Jefferson",
  "TOOKOFFICE" : "1797-03-04",
  "LEFTOFFICE" : "1801-03-04"

Thomas Jefferson was the 2nd vice-president of the United States (and 3rd president). He took office as vice-president on the 4th of March 1797.


Hazelcast can be configured from code, from XML, from Spring, and now from YAML. Pick what suits you, it’s not exactly important.

JSON can be queried efficiently, the grid knows how to parse JSON objects. You can search for records in a memory-based data-grid (Hazelcast!) faster than a disk-based system.

If you have a legacy store, you can have an optimized data loader that understands how to translate the data model as relational tables into JSON. Or you can do as we do here, a generic routine that decides what to do. Pick which fits your need.

Download the code from Hazelcast Code Samples.

About the Author

About the Author

Neil Stevenson

Neil Stevenson

CTO, Hazelcast Platform

Neil is a solution architect for Hazelcast®, is the industry leading in-memory computing platform. In more than 30 years of work in IT, Neil has designed, developed and debugged a number of software systems for companies large and small.

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