This doesn't change Hadoop from batch processing to real time - it's a new query engine that uses the same data sources/formats as Hadoop and the same interfaces as Hive. So yeah, similar idea to Google Dremel / F1.
You're right that the data is still stored in HDFS, and you can access the data that was originally accessed in Hive. However, because of the way Impala works, it does in fact provide a real-time query interface for data that is sitting in Hadoop (HDFS). The back-end is written in C++ and accesses/processes data much differently than MapReduce which allows for some pretty awesome performance numbers :-)
My reading was that columnar storage was a critical piece of Dremel. "Accessing the same data as Hive" doesn't sound like Impala is doing that. Is that optional then or is this just a query engine?
I can't say for sure, but I believe Doug Cutting's Trevni format is already in the beta but it's a slow version. Impala is initially focusing on architecture, I'm sure columnar format will have attention eventually.
This looks very interesting. I've got multiple PB of log files that I'd like to be able to do real-time and batch processing on. Log files are being generated at a small number of Gbps.
Anyone else got that much data and have a good solution?
What kind of analysis are you trying to do on that dataset? Log files can be verbose as well. Do you need all the data in the logs or can you cut down the data in each row to only what you need?
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[ 6.2 ms ] story [ 51.3 ms ] threadThis aint nothing like that.
https://github.com/cutting/trevni
Anyone else got that much data and have a good solution?
Impala source code: https://github.com/cloudera/impala
Impala documentation can be found here: https://ccp.cloudera.com/display/IMPALA10BETADOC/Cloudera+Im...
http://metamarkets.com/category/technology/druid/
http://blog.cloudera.com/blog/2012/10/cloudera-impala-real-t...