# Hunk vs ElastisSearch
**URL:** https://discuss.elastic.co/t/hunk-vs-elastissearch/31726
**Category:** Elasticsearch
**Created:** [October 6, 2015, 7:41pm UTC](https://discuss.elastic.co/t/hunk-vs-elastissearch/31726 "2015-10-06T19:41:01Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@mkarthikswamy](https://discuss.elastic.co/u/mkarthikswamy)
#### Post date: [October 6, 2015, 7:41pm UTC](https://discuss.elastic.co/t/hunk-vs-elastissearch/31726/1 "2015-10-06T19:41:01Z")
Hi,
We are exploring the applicability of using ElasticSearch for a data reconciliation project that we are delivering.
The details are as follows:
- The project currently uses HUNK
- The data sources are as follows: Multiple Oracle table data through
Golden Fate replication, some files SFTPd into the cluster, some data
coming through JMS queue
- The data structures are XML, JSON, CSV
- We need to show data reconciliation reports by reconciling across all
the data sources, arbitrary correlation across the data sources
- The cluster already uses MapR Hadoop
I have the following questions regarding ElasticSearch:
-Does ElasticSearch fit the above reconciliation usecase?
- Should we run ElasticSearch as a indedepndent cluster or use MapR Hadoop as the storage for ES? What are the pros and cons?
- Can ES ingest data in the XML, JSON, CSV formats? If there is a newer format, how does it work , do we write an adaptor?
- I see that Hunk shoots MapReduce jobs for every query which increases
the latency. What does ES do when a query is done, how is it faster than
Hunk, if it is?
- Do you have any field reports on Hunk swap out using ES?
- Does ES perform better with MapR because of the MapRFS random r/w capability? I would assume yes, but want to confirm
Thanks for your support!
MK
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### Author:  [@warkolm](https://discuss.elastic.co/u/warkolm)
#### Post date: [October 6, 2015, 8:47pm UTC](https://discuss.elastic.co/t/hunk-vs-elastissearch/31726/2 "2015-10-06T20:47:05Z")
Hadoop and ES run in parallel and offer different functionality, so it's not possible to do a direct comparison.
That said, ES (via Logstash) can deal with XML/CSV and RDBMS, and it does JSON natively so it sounds like it would fit the use case.
You cannot store ES on hadoop, it's s standalone product that can be integrated using the es-hadoop connector, but you can't store the ES data on HDFS.
You will probably find that ES is a lot more responsive around querying as it's more realtime. You'd have to test on your systems and data to get an idea though.
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### Author:  [@system](https://discuss.elastic.co/u/system)
#### Post date: [July 5, 2017, 11:46pm UTC](https://discuss.elastic.co/t/hunk-vs-elastissearch/31726/3 "2017-07-05T23:46:23Z")