--- layout: doc_page --- # Tutorial: All About Queries Hello! This tutorial is meant to provide a more in-depth look into Druid queries. The tutorial is somewhat incomplete right now but we hope to add more content to it in the near future. Setup ----- Before we start digging into how to query Druid, make sure you've gone through the other tutorials and are comfortable with spinning up a local cluster and loading data into Druid. #### Booting a Druid Cluster Let's start up a simple Druid cluster so we can query all the things. Note: If Zookeeper and metadata storage aren't running, you'll have to start them again as described in [The Druid Cluster](Tutorial%3A-The-Druid-Cluster.html). To start a Coordinator node: ```bash java -Xmx256m -Duser.timezone=UTC -Dfile.encoding=UTF-8 -classpath config/_common:config/coordinator:lib/* io.druid.cli.Main server coordinator ``` To start a Historical node: ```bash java -Xmx256m -Duser.timezone=UTC -Dfile.encoding=UTF-8 -classpath config/_common:config/historical:lib/* io.druid.cli.Main server historical ``` To start a Broker node: ```bash java -Xmx256m -Duser.timezone=UTC -Dfile.encoding=UTF-8 -classpath config/_common:config/broker:lib/* io.druid.cli.Main server broker ``` Querying Your Data ------------------ Make sure you've completed [Loading Your Data](Loading-Your-Data-Part-1.html) so we have some data to query. Having done that, it's time to query our data! For a complete specification of queries, see [Querying](Querying.html). #### Construct a Query ```json { "queryType": "groupBy", "dataSource": "wikipedia", "granularity": "all", "dimensions": [], "aggregations": [ {"type": "count", "name": "rows"}, {"type": "longSum", "name": "edit_count", "fieldName": "count"}, {"type": "doubleSum", "name": "chars_added", "fieldName": "added"} ], "intervals": ["2010-01-01T00:00/2020-01-01T00"] } ``` #### Query That Data Run the query against your broker: ```bash curl -X POST "http://localhost:8082/druid/v2/?pretty" -H 'Content-type: application/json' -d @query.body ``` And get: ```json [ { "version" : "v1", "timestamp" : "2010-01-01T00:00:00.000Z", "event" : { "chars_added" : 1545.0, "edit_count" : 5, "rows" : 5 } } ] ``` This result tells us that our query has 5 edits, and we have 5 rows of data as well. In those 5 edits, we have 1545 characters added. #### What can I query for? How are we to know what queries we can run? Although [Querying](Querying.html) is a helpful index, to get a handle on querying our data we need to look at our ingestion schema. There are a few particular fields we care about in the ingestion schema. All of these fields should in present in the real-time ingestion schema and the batch ingestion schema. Datasource: ```json "dataSource":"wikipedia" ``` Our dataSource tells us the name of the relation/table, or 'source of data'. What we decide to name our data source must match the data source we are going to be querying. Granularity: ```json "indexGranularity": "none", ``` Druid will roll up data at ingestion time unless the index/rollup granularity is specified as "none". Your query granularity cannot be lower than your index granularity. Aggregators: ```json "aggregators" : [{ "type" : "count", "name" : "count" }, { "type" : "doubleSum", "name" : "added", "fieldName" : "added" }, { "type" : "doubleSum", "name" : "deleted", "fieldName" : "deleted" }, { "type" : "doubleSum", "name" : "delta", "fieldName" : "delta" }] ``` The [Aggregations](Aggregations.html) specified at ingestion time correlated directly to the metrics that can be queried. Dimensions: ```json "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"] ``` These specify the dimensions that we can filter our data on. If we added a dimension to our groupBy query, we get: ```json { "queryType": "groupBy", "dataSource": "wikipedia", "granularity": "all", "dimensions": ["namespace"], "aggregations": [ {"type": "longSum", "name": "edit_count", "fieldName": "count"}, {"type": "doubleSum", "name": "chars_added", "fieldName": "added"} ], "intervals": ["2010-01-01T00:00/2020-01-01T00"] } ``` Which gets us data grouped over the namespace dimension in return! ```json [ { "version" : "v1", "timestamp" : "2010-01-01T00:00:00.000Z", "event" : { "chars_added" : 180.0, "edit_count" : 2, "namespace" : "article" } }, { "version" : "v1", "timestamp" : "2010-01-01T00:00:00.000Z", "event" : { "chars_added" : 1365.0, "edit_count" : 3, "namespace" : "wikipedia" } } ] ``` Additionally,, we can also filter our query to narrow down our metric values: ```json { "queryType": "groupBy", "dataSource": "wikipedia", "granularity": "all", "filter": { "type": "selector", "dimension": "namespace", "value": "article" }, "aggregations": [ {"type": "longSum", "name": "edit_count", "fieldName": "count"}, {"type": "doubleSum", "name": "chars_added", "fieldName": "added"} ], "intervals": ["2010-01-01T00:00/2020-01-01T00"] } ``` Which gets us metrics about only those edits where the namespace is 'article': ```json [ { "version" : "v1", "timestamp" : "2010-01-01T00:00:00.000Z", "event" : { "chars_added" : 180.0, "edit_count" : 2 } } ] ``` Check out [Filters](Filters.html) for more information. What Types of Queries to Use ---------------------------- The types of query you should use depends on your use case. [TimeBoundary queries](TimeBoundaryQuery.html) are useful to understand the range of your data. [Timeseries queries](TimeseriesQuery.html) are useful for aggregates and filters over a time range, and offer significant speed improvements over [GroupBy queries](GroupByQuery.html). To find the top values for a given dimension, [TopN queries](TopNQuery.html) should be used over group by queries as well. ## Learn More ## You can learn more about querying at [Querying](Querying.html)! If you are ready to evaluate Druid more in depth, check out [Booting a production cluster](Booting-a-production-cluster.html)!