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A few of us were talking about ways to speed up the `date_histogram` using the index for the timestamp rather than the doc values. To do that we'd have to pre-compute all of the "round down" points in the index. It turns out that *just* precomputing those values speeds up rounding fairly significantly: ``` Benchmark (count) (interval) (range) (zone) Mode Cnt Score Error Units before 10000000 calendar month 2000-10-28 to 2000-10-31 UTC avgt 10 96461080.982 ± 616373.011 ns/op before 10000000 calendar month 2000-10-28 to 2000-10-31 America/New_York avgt 10 130598950.850 ± 1249189.867 ns/op after 10000000 calendar month 2000-10-28 to 2000-10-31 UTC avgt 10 52311775.080 ± 107171.092 ns/op after 10000000 calendar month 2000-10-28 to 2000-10-31 America/New_York avgt 10 54800134.968 ± 373844.796 ns/op ``` That's a 46% speed up when there isn't a time zone and a 58% speed up when there is. This doesn't work for every time zone, specifically those that have two midnights in a single day due to daylight savings time will produce wonky results. So they don't get the optimization. Second, this requires a few expensive computation up front to make the transition array. And if the transition array is too large then we give up and use the original mechanism, throwing away all of the work we did to build the array. This seems appropriate for most usages of `round`, but this change uses it for *all* usages of `round`. That seems ok for now, but it might be worth investigating in a follow up. I ran a macrobenchmark as well which showed an 11% preformance improvement. *BUT* the benchmark wasn't tuned for my desktop so it overwhelmed it and might have produced "funny" results. I think it is pretty clear that this is an improvement, but know the measurement is weird: ``` Benchmark (count) (interval) (range) (zone) Mode Cnt Score Error Units before 10000000 calendar month 2000-10-28 to 2000-10-31 UTC avgt 10 96461080.982 ± 616373.011 ns/op before 10000000 calendar month 2000-10-28 to 2000-10-31 America/New_York avgt 10 g± 1249189.867 ns/op after 10000000 calendar month 2000-10-28 to 2000-10-31 UTC avgt 10 52311775.080 ± 107171.092 ns/op after 10000000 calendar month 2000-10-28 to 2000-10-31 America/New_York avgt 10 54800134.968 ± 373844.796 ns/op Before: | Min Throughput | hourly_agg | 0.11 | ops/s | | Median Throughput | hourly_agg | 0.11 | ops/s | | Max Throughput | hourly_agg | 0.11 | ops/s | | 50th percentile latency | hourly_agg | 650623 | ms | | 90th percentile latency | hourly_agg | 821478 | ms | | 99th percentile latency | hourly_agg | 859780 | ms | | 100th percentile latency | hourly_agg | 864030 | ms | | 50th percentile service time | hourly_agg | 9268.71 | ms | | 90th percentile service time | hourly_agg | 9380 | ms | | 99th percentile service time | hourly_agg | 9626.88 | ms | |100th percentile service time | hourly_agg | 9884.27 | ms | | error rate | hourly_agg | 0 | % | After: | Min Throughput | hourly_agg | 0.12 | ops/s | | Median Throughput | hourly_agg | 0.12 | ops/s | | Max Throughput | hourly_agg | 0.12 | ops/s | | 50th percentile latency | hourly_agg | 519254 | ms | | 90th percentile latency | hourly_agg | 653099 | ms | | 99th percentile latency | hourly_agg | 683276 | ms | | 100th percentile latency | hourly_agg | 686611 | ms | | 50th percentile service time | hourly_agg | 8371.41 | ms | | 90th percentile service time | hourly_agg | 8407.02 | ms | | 99th percentile service time | hourly_agg | 8536.64 | ms | |100th percentile service time | hourly_agg | 8538.54 | ms | | error rate | hourly_agg | 0 | % | ```
= Elasticsearch == A Distributed RESTful Search Engine === https://www.elastic.co/products/elasticsearch[https://www.elastic.co/products/elasticsearch] Elasticsearch is a distributed RESTful search engine built for the cloud. Features include: * Distributed and Highly Available Search Engine. ** Each index is fully sharded with a configurable number of shards. ** Each shard can have one or more replicas. ** Read / Search operations performed on any of the replica shards. * Multi Tenant. ** Support for more than one index. ** Index level configuration (number of shards, index storage, ...). * Various set of APIs ** HTTP RESTful API ** All APIs perform automatic node operation rerouting. * Document oriented ** No need for upfront schema definition. ** Schema can be defined for customization of the indexing process. * Reliable, Asynchronous Write Behind for long term persistency. * (Near) Real Time Search. * Built on top of Apache Lucene ** Each shard is a fully functional Lucene index ** All the power of Lucene easily exposed through simple configuration / plugins. * Per operation consistency ** Single document level operations are atomic, consistent, isolated and durable. == Getting Started First of all, DON'T PANIC. It will take 5 minutes to get the gist of what Elasticsearch is all about. === Installation * https://www.elastic.co/downloads/elasticsearch[Download] and unpack the Elasticsearch official distribution. * Run `bin/elasticsearch` on Linux or macOS. Run `bin\elasticsearch.bat` on Windows. * Run `curl -X GET http://localhost:9200/` to verify Elasticsearch is running. === Indexing First, index some sample JSON documents. The first request automatically creates the `my-index-000001` index. ---- curl -X POST 'http://localhost:9200/my-index-000001/_doc?pretty' -H 'Content-Type: application/json' -d ' { "@timestamp": "2099-11-15T13:12:00", "message": "GET /search HTTP/1.1 200 1070000", "user": { "id": "kimchy" } }' curl -X POST 'http://localhost:9200/my-index-000001/_doc?pretty' -H 'Content-Type: application/json' -d ' { "@timestamp": "2099-11-15T14:12:12", "message": "GET /search HTTP/1.1 200 1070000", "user": { "id": "elkbee" } }' curl -X POST 'http://localhost:9200/my-index-000001/_doc?pretty' -H 'Content-Type: application/json' -d ' { "@timestamp": "2099-11-15T01:46:38", "message": "GET /search HTTP/1.1 200 1070000", "user": { "id": "elkbee" } }' ---- === Search Next, use a search request to find any documents with a `user.id` of `kimchy`. ---- curl -X GET 'http://localhost:9200/my-index-000001/_search?q=user.id:kimchy&pretty=true' ---- Instead of a query string, you can use Elasticsearch's https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html[Query DSL] in the request body. ---- curl -X GET 'http://localhost:9200/my-index-000001/_search?pretty=true' -H 'Content-Type: application/json' -d ' { "query" : { "match" : { "user.id": "kimchy" } } }' ---- You can also retrieve all documents in `my-index-000001`. ---- curl -X GET 'http://localhost:9200/my-index-000001/_search?pretty=true' -H 'Content-Type: application/json' -d ' { "query" : { "match_all" : {} } }' ---- During indexing, Elasticsearch automatically mapped the `@timestamp` field as a date. This lets you run a range search. ---- curl -X GET 'http://localhost:9200/my-index-000001/_search?pretty=true' -H 'Content-Type: application/json' -d ' { "query" : { "range" : { "@timestamp": { "from": "2099-11-15T13:00:00", "to": "2099-11-15T14:00:00" } } } }' ---- === Multiple indices Elasticsearch supports multiple indices. The previous examples used an index called `my-index-000001`. You can create another index, `my-index-000002`, to store additional data when `my-index-000001` reaches a certain age or size. You can also use separate indices to store different types of data. You can configure each index differently. The following request creates `my-index-000002` with two primary shards rather than the default of one. This may be helpful for larger indices. ---- curl -X PUT 'http://localhost:9200/my-index-000002?pretty' -H 'Content-Type: application/json' -d ' { "settings" : { "index.number_of_shards" : 2 } }' ---- You can then add a document to `my-index-000002`. ---- curl -X POST 'http://localhost:9200/my-index-000002/_doc?pretty' -H 'Content-Type: application/json' -d ' { "@timestamp": "2099-11-16T13:12:00", "message": "GET /search HTTP/1.1 200 1070000", "user": { "id": "kimchy" } }' ---- You can search and perform other operations on multiple indices with a single request. The following request searches `my-index-000001` and `my-index-000002`. ---- curl -X GET 'http://localhost:9200/my-index-000001,my-index-000002/_search?pretty=true' -H 'Content-Type: application/json' -d ' { "query" : { "match_all" : {} } }' ---- You can omit the index from the request path to search all indices. ---- curl -X GET 'http://localhost:9200/_search?pretty=true' -H 'Content-Type: application/json' -d ' { "query" : { "match_all" : {} } }' ---- === Distributed, highly available Let's face it, things will fail.... Elasticsearch is a highly available and distributed search engine. Each index is broken down into shards, and each shard can have one or more replicas. By default, an index is created with 1 shard and 1 replica per shard (1/1). There are many topologies that can be used, including 1/10 (improve search performance), or 20/1 (improve indexing performance, with search executed in a map reduce fashion across shards). In order to play with the distributed nature of Elasticsearch, simply bring more nodes up and shut down nodes. The system will continue to serve requests (make sure you use the correct http port) with the latest data indexed. === Where to go from here? We have just covered a very small portion of what Elasticsearch is all about. For more information, please refer to the https://www.elastic.co/products/elasticsearch[elastic.co] website. General questions can be asked on the https://discuss.elastic.co[Elastic Forum] or https://ela.st/slack[on Slack]. The Elasticsearch GitHub repository is reserved for bug reports and feature requests only. === Building from source Elasticsearch uses https://gradle.org[Gradle] for its build system. In order to create a distribution, simply run the `./gradlew assemble` command in the cloned directory. The distribution for each project will be created under the `build/distributions` directory in that project. See the xref:TESTING.asciidoc[TESTING] for more information about running the Elasticsearch test suite. === Upgrading from older Elasticsearch versions In order to ensure a smooth upgrade process from earlier versions of Elasticsearch, please see our https://www.elastic.co/guide/en/elasticsearch/reference/current/setup-upgrade.html[upgrade documentation] for more details on the upgrade process.
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