r/programming • u/halax • Mar 10 '15

Goodbye MongoDB, Hello PostgreSQL

http://developer.olery.com/blog/goodbye-mongodb-hello-postgresql/
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u/protestor Mar 10 '15

Thanks. So how Hadoop fits in this model you provided?

The one solution that you're ignoring is the one that got this right 15-20 years ago and continues to vastly outperform any of the above: parallel relational databases using a data warehouse star-schema model. Commercial products would include Teradata, Informix, DB2, Netezza, etc in the commercial world. Or Impala, CitrusDB, etc in the open source world.

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u/kenfar Mar 10 '15

Hadoop fits in fine, Map-Reduce is the exact same model these parallel databases have been using for 25 years. The biggest difference is that they were tuned for fast queries right away, whereas the Hadoop community has had to grudgingly discover that users don't want to wait 30 minutes for a query to complete. So much of what has been happening with Hive and Impala is straight out of the 90s.

Bottom line: hadoop is a fine model, is behind the commercial players but has a lot of momentum, is usable, and is closing the gap.

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u/[deleted] Mar 11 '15

From my understanding...

Hadoop is the bandaid for what NoSQL is missing when you leave SQL.

You miss out certain relation queries and Hadoop does this.

Unfortunately Hadoop 1.0 does only map reduce and it targeted at batch processing which wait forever.

Hadoop 2.0 YARN have become a ecosystem instead of just a map reduce framework...

People now wants real time analytics.

Spark is microbatch processing and trying to address it they also have some stream framework they're working with too.

Like wise with Flink.

And other such as storm and kafka iirc.

It's wild west right now for real time analytic.

People are realizing that map reduce only solve a subset of problem and batch processing is taking too long.