Getting Data into Hadoop in real-time

Moving data between databases is hard. Without ever intending it, I seem to have spent a lifetime working on solutions for getting data into and out of databases, but more frequently between. In fact, my first job out of university was migrating data from BRS/Text, a free-text database (probably what we would call a NoSQL) into a more structured Oracle.
Today I spend some of my time working in Big Data, more often than not, migrating information from existing data stores into Big Data so that they can be analysed, something I covered in more detail here:
The problem with the current techniques, Sqoop included, is that they rely on a relatively manual, even basic, transfer process. Dump your data out, reload it back again into Hadoop.
Even with Sqoop, although it automates much of the process, it is not entirely reliable, especially if you want to do more than simple dump and load. Serial loading, or incrementally transferring data from MySQL or Oracle, is fraught with problems, not least of which is that it requires adding a timestamp to your data structure to get the best results out of it.
Perhaps worse though is that Sqoop is an intermittent, even periodic transfer system. Incremental loading works by copying all the changed records since a specific point in time. Running it too frequently is counter productive, which means you end up using a 15-minute or every-couple-of-hour period, depending on your database activity.
Most databases have some kind of stream of changes that enables you to see everything that has happened on the database. With MySQL, that’s the binary log. And with the Open Source Tungsten Replicator tool we take advantage of that so that we can replicate into MySQL, and indeed into Oracle, MongoDB and Vertica, among others.
Reading the data out from MySQL is lightweight since the master just reads the contents of the binary log; especially compared to Sqoop, which uses read locks and SELECT * with and without LIMIT clauses.
Right now we’re working on an applier that writes that data into Hadoop in real time from MySQL. Unlike Sqoop, we provide a continuous stream of changes from MySQL into the immutable store of Hadoop.
But the loading and nature of Hadoop presents some interesting issues, not least of which (if you’ve been following my other articles) is the fact that data written into Hadoop is immutable. For data that is constantly changing, an immutable store is not the obvious destination.
We get round that by using the batch loading system to create CSV files that contain the data, changes and sequence numbers, and then loading that information into Hadoop. In fact, Robert has updated the batch loader to use a new JavaScript based system (of which more in a future blog post) that simplifies the entire process, without requiring a direct connection or interface to Hadoop (although we can write directly into HDFS).
For example, the MySQL row:

| 3 | #1 Single | 2006 | Cats and Dogs (#1.4) |

Is represented within the staging files generated as:

I^A1318^A3^A3^A#1 Single^A2006^ACats and Dogs (#1.4)

That’s directly accessible by Hive. In fact, using our ddlscan tool, we can even create the Hive table definitions for you:

ddlscan -user tungsten -url 'jdbc:mysql://host1:13306/test' -pass password \
-template ddl-mysql-hive-0.10.vm -db test

Then we can use that record of changes to create a live version of the data, using a straightforward query within Hive. In fact, Hive provides the final crucial stage of the loading process by giving us that live view of the change data, and we simplify that element by providing the core data, and ensuring that the CSV data is in the right format for Hive to use the files without changes.
The process is quite remarkable; speed-wise for direct dumps, Tungsten Replicator is comparable to Sqoop, but when it comes to change data, the difference is that we have the information in real time. You don’t have to wait for the next Sqoop load, or for the incremental loading and row selection of Sqoop, instead, we just apply the changes written into the binary log.
Of course, we can fine tune the intervals of the writes of the CSV change data into Hadoop using the block commit properties (see For example, this means by default we commit into Hadoop every 10s or 10,000 rows, but we can change it to commit every 5s or 1,000 rows if your data is critical and busy.
We’re still optimising and improving the system, but I can tell you that in my own tests we can handle GB of change data and information in a live fashion, both across single-table and multi-table/multi-schema datasets. What’s particularly cool is that if you are using Hadoop as a concentrator for all of your MySQL data for analysis, we can transfer from multiple MySQL servers into Hadoop simultaneously and take advantage of the multi-node Hadoop environment to cope with the load.