# Search and Index updates in parallel

**URL:** <https://discuss.elastic.co/t/search-and-index-updates-in-parallel/14842>\
**Category:** Elasticsearch\
**Created:** [December 12, 2013, 7:53pm UTC](https://discuss.elastic.co/t/search-and-index-updates-in-parallel/14842 "2013-12-12T19:53:12Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![Bijuv\_V](https://avatars.discourse-cdn.com/v4/letter/b/b77776/32.png) [@Bijuv\_V](https://discuss.elastic.co/u/Bijuv_V)\
**Post date:** [December 12, 2013, 7:53pm UTC](https://discuss.elastic.co/t/search-and-index-updates-in-parallel/14842/1 "2013-12-12T19:53:12Z")

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Hi,

I want to update the elastic search index very frequently as the data that  
is indexed is volatile.

So my question is , when the index is getting updated, will the search  
query that is happening on the same index have an impact on the  
performance? If there is no impact on the performance, how does Elastic  
Search achieve the same?

If this is already documented, a pointer to the documentation will also  
help.

Regards

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<div class="post-metadata">

**Author:** ![brian\_yoder](https://avatars.discourse-cdn.com/v4/letter/b/f1d935/32.png) [@brian\_yoder](https://discuss.elastic.co/u/brian_yoder)\
**Post date:** [December 12, 2013, 8:21pm UTC](https://discuss.elastic.co/t/search-and-index-updates-in-parallel/14842/2 "2013-12-12T20:21:22Z")

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Updates followed by queries on fields will lag a bit, depending on the  
default index interval. But a get-by-id will be realtime.

For example, I wrote a performance test case in which I created a custom  
mapping with no indexing on the fields. My driver had one or more writer  
threads that created unique \_id values and added documents to the index +  
type + \_id. The writers then passed the \_id values onto one or more reader  
threads that queried based on index + type + \_id. Also, all of the  
documents in the index + type were given a 10s time to live, and the  
default TTL processing was left at 60s. (This meant that expired documents  
would remain for up to about 50s).

No errors were seen; no update failures, and no query failures. ES and its  
use of the JVM were solid, showing steady and sustainable throughput.

On a quad-core i7 MacBook Pro laptop (disk spindles, not SSD), I could  
sustain a throughput of about 5000 documents created, added, and queried  
per second. This also included serialization to and from JSON. I used the  
Jackson 2 libraries and Jackson data binding to do the serialization. (ES  
provides only the streaming API of Jackson 1.9. It would seem a good update  
for ES 1.0 to include the full (3 jars worth) of Jackson 2. But if not,  
then the jars are easy to add, as ES nicely re-packages all of its internal  
dependent jars to avoid conflicts:

jackson-annotations-2.2.3.jar  
jackson-core-2.2.3.jar  
jackson-databind-2.2.3.jar

Hope this helps. It's not a typical use case, but one of the several  
important ones to me.

Regards,  
Brian

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**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\
**Post date:** [July 6, 2017, 2:01am UTC](https://discuss.elastic.co/t/search-and-index-updates-in-parallel/14842/3 "2017-07-06T02:01:37Z")

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