# Scaling time series indices **URL:** \ **Category:** Elasticsearch\ **Created:** [July 10, 2016, 5:02pm UTC](https://discuss.elastic.co/t/scaling-time-series-indices/55135 "2016-07-10T17:02:27Z")\ **Posts on this page:** 1\ **Showing post:** 1
**Author:** ![shushu](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/shushu/32/10037_2.png) [@shushu](https://discuss.elastic.co/u/shushu)\ **Post date:** [July 10, 2016, 5:02pm UTC](https://discuss.elastic.co/t/scaling-time-series-indices/55135/1 "2016-07-10T17:02:27Z")
Hi, I am running a time series elasticsearch cluster (on top of AWS service). Using template, I create a daily index. 5 shards, 2 replicas, on 10 nodes + 4 masters. Once the number of clients went high up - all stopped working - CPU maximum went to 100%, while CPU average kept low (~40%). My guess is that the main searches are done against the latest days, so it focus on the nodes that has latest data - while the rest stays idle. My question is - what would be the right scale mechanism ? I think that I should by default, in the template, give the maximum number of replicas (10), so latest data will have as many replicas as possible. Once data become old - in couple of days - reduce the number of replicas to 2. Is this sounds like a decent methodology ? Any other recommendations ? --- _[View the full topic](https://discuss.elastic.co/t/scaling-time-series-indices/55135)._