The document discusses a method for migrating structured data between Hadoop and RDBMS using an analytics toolbox called Bouquet, which generates SQL via a REST API. It highlights the integration of various technologies including Spark, Kafka, and Avro schema for efficient data management and enrichment. Challenges in implementation, as well as future improvements for scalability and functionality, are also outlined.

1. February 16th
2016
louis.rabiet@squidsolutions.com
Migrating structured data between Hadoop
and RDBMS

2. Who am I?
• Full Stack engineer at Squid Solutions.
• Specialised in Big data.
• Fun fact: sleeping by myself in my tent on
the top of the highest mountains of the world

3. What I do ?
• Develop of an analytics toolbox.
• No setup. No SQL. No compromise.
• Generate SQL with a REST API.
It is open source!
https://github.com/openbouquet

4. Topic of today
• You need Scalability?
• You need a machine learning toolbox?
Hadoop is the solution.
•But you still need structured data?
Our tool provide a solution.
=> We need both!

5. What does that mean?
• Creation of dataset in Bouquet
• Send the dataset to Spark
• Enrich inside Spark
• Re-injection in original database

6. How we do it?
User input
Relational
DB
SparkBouquet

7. Create and Send

8. How does it work?
BouquetRelational
DB
Spark
HDFS/
Tachyon
Hive
Metastore
User select the data. Bouquet generate the corresponding SQL Code
Kafka

9. How does it work?
BouquetRelational
DB
Spark
HDFS/
Tachyon
Hive
Metastore
Data is read from the SQL database
Kafka

10. How does it work?
BouquetRelational
DB
Spark
HDFS/
Tachyon
Hive
Metastore
The BI tool creates an avro schema and send the data to Kafka
Kafka

11. How does it work?
BouquetRelational
DB
Spark
Kafka
HDFS/
Tachyon
Hive
Metastore
Kafka Broker(s) receive the data

12. How does it work?
BouquetRelational
DB
Spark
HDFS/
Tachyon
Hive
Metastore
Kafka
The hive metastore is updated and the hdfs connectors writes into hdfs

13. How to keep the data structured?
Use a schema registry (Avro in Kafka).
each schema has a corresponding kafka topic and a distinct hive table.
{
"type": "record",
"name": "ArtistGender",
"fields" : [
{"name": "count", "type": "long"},
{"name": "gender", "type": "String"]}
]
}

14. Challenges
- Auto creation of topics/table in Hive for each datasets from Bouquet.
- JDBC reads are too slow for something like Kafka.
- Issue with types conversion: null is not supported for all cases for example (issue
272 on schema-registry).
- Versions: Kafka 0.9.0, Tachyon 0.7.1, Spark 1.5.2 with HortonWorks 2.3.4 (Dec
2015)
- Hive: Setting the warehouse directory.
- In tachyon: Setting up hostname.

15. Tachyon?
• Use it as in memory filesystem to replace
HDFS.
• Interact with Spark using the hdfs plugin.
• Transparent from user point of view

16. Status
Injection SQL -> Spark: OK
Spark usage: OK
Re-injection: In alpha stage.

17. Re-injection
Two solutions:
• Spark user notifies Bouquet that data has
changed (using a custom function)
• Bouquet pulls the data from spark

18. We use it for real!
Collaborating with La Poste to be able to
use Spark and the re-injection mechanism
to use Bouquet and a geographical
visualisation.

19. In the future
• Notebook integration
• We got a DSL for bouquet API, we may
want to have built-in support spark.
• Improve scalability (Bulk Unload and
Kafka fine tuning)

20. QUESTIONS
OPENBOUQUET.IO

21. DB HD
Bouquet Architecture
Bouquet Server
SQL DATA
JDBC
Dynamic Caching
& Indexing
REST APIBusiness Modeling OAuth2
Generic Apps
Multi-Tenant
REDIS Elastic MongoDB
JS/SDK Custom Apps