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Notes about Hadoop

My notes from two presentations given to the data mining SIG of the local ACM chapter.

Hadoop is a scalable fault-tolerant grid operating system for data storage and processing.

It is not a database. It is more similar to an operating system: Hadoop has a file system (HDFS) and a job scheduler (Map-Reduce). Both are distributed. You can load any kind of data into Hadoop.

It is quite popular – the last Hadoop Summit had 750 attendees. Not bad for a new open-source technology. It is also quite efficient for some tasks. Hadoop cluster of 1460 nodes can sort a Terabyte of data in 62 seconds – currently the world record for sorting a terabyte.

Hadoop Design Axioms:

  • System will manage and heal itself. (Because using commodity hardware – failure is inevitable).
  • Performance will scale linearly. (With few limitations).
  • Compute should move to data (Processing job should run on the machine holding the data to process)
  • Simple core. Modular and extensible

Distributed file system. Block size is 64M (!). User configures replication factor – each block is replicated on K machines (K chosen by user). More replication can be configured for hot blocks.
A name node keeps track of the blocks and if a node fails the data on it will be replicated to other nodes.

Distributes jobs. It tried to run jobs local to their data to avoid network overhead. It also detects failures and even servers running behind on the processing. If a part of the job is lagging in processing, it will start copies of this part of the job on other servers with the hope that one of the copies will finish faster.

Hadoop Ecosystem:

  • HBase: Google’s big table implementation. Key-value based. Good for quick lookups, but not batch processing. Transactional.
  • Pig, Hive, Scoop: Different languages. Map-Reduce is like assembly – High performance, low-level, contains too much details for most tasks. Hive is SQL language for Hadoop.

Hadoop vs. RDBMS?
RDBMS – expensive, structured, fast, interactive, has standards, transactional.
Hadoop – affordable, unstructured, scalable, resilient. Solves both storage and processing.

Hive and Hadoop at Facebook
Facebook got 200GB of data each day as of March 2008. Thats a lot of data to manage. Facebook philosophy is that more insights can be achieved from running simpler algorithms on more data.

Why Hadoop? Cost of storage. Limitations of data-analysis systems. Many systems have limited scalability. And they were closed and propitiatory.

Why not map-reduce? Not many developers have experience with it. Needed well known schemas and structure.

Hive was built on top of Hadoop to solve these problems. It saves metadata and adds SQL. Also allows integrating with other systems. Hive has tables, which have partitions which hold buckets. Buckets are used for sampling. Hive is very extensible. You can have user defined functions, types, objects, etc.

Hive does optimizations – join order, different processing for skewed data. The optimizer is rule based and uses hints. It also does some kind of dynamic sampling. You can look at the explain plans for the jobs and use that for tuning. Hive uses columnar compression.

Hive support integrations with JDBC, ODBC and Thrift.

It lacks resource management and needs monitoring to catch and kill “bad” jobs.

Concurrency wise, the idea is that you insert data, “publish” it and from the moment it is published everyone else can see it – but it cannot be modified or deleted. This means no read/write contention.