[GitHub] [druid] jihoonson commented on a change in pull request #9704: Refresh query docs.

[GitBox <gi...@apache.org>]

jihoonson commented on a change in pull request #9704: Refresh query docs.
URL: https://github.com/apache/druid/pull/9704#discussion_r409078660
 
 

 ##########
 File path: docs/querying/datasource.md
 ##########
 @@ -22,43 +22,317 @@ title: "Datasources"
   ~ under the License.
   -->
 
+Datasources in Apache Druid are things that you can query. The most common kind of datasource is a table datasource,
+and in many contexts the word "datasource" implicitly refers to table datasources. This is especially true
+[during data ingestion](../ingestion/index.html), where ingestion is always creating or writing into a table
+datasource. But at query time, there are many other types of datasources available.
 
-A data source is the Apache Druid equivalent of a database table. However, a query can also masquerade as a data source, providing subquery-like functionality. Query data sources are currently supported only by [GroupBy](../querying/groupbyquery.md) queries.
+In the [Druid SQL](sql.html) language, datasources are provided in the [`FROM` clause](sql.html#from).
 
-### Table datasource
-The table data source is the most common type. It's represented by a string, or by the full structure:
+The word "datasource" is generally spelled `dataSource` (with a capital S) when it appears in API requests and
+responses.
 
+## Datasource type
+
+### `table`
+
+<!--DOCUSAURUS_CODE_TABS-->
+<!--SQL-->
+```sql
+SELECT column1, column2 FROM "druid"."dataSourceName"
+```
+<!--Native-->
+```json
+{
+  "queryType": "scan",
+  "dataSource": "dataSourceName",
+  "columns": ["column1", "column2"],
+  "intervals": ["0000/3000"]
+}
+```
+<!--END_DOCUSAURUS_CODE_TABS-->
+
+The table datasource is the most common type. This is the kind of datasource you get when you perform
+[data ingestion](../ingestion/index.html). They are split up into segments, distributed around the cluster,
+and queried in parallel.
+
+In [Druid SQL](sql.html#from), table datasources reside in the the `druid` schema. This is the default schema, so table
+datasources can be referenced as either `druid.dataSourceName` or simply `dataSourceName`.
+
+In native queries, table datasources can be referenced using their names as strings (as in the example above), or by
+using JSON objects of the form:
+
+```json
+"dataSource": {
+  "type": "table",
+  "name": "dataSourceName"
+}
+```
+
+To see a list of all table datasources, use the SQL query
+`SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = 'druid'`.
+
+### `lookup`
+
+<!--DOCUSAURUS_CODE_TABS-->
+<!--SQL-->
+```sql
+SELECT k, v FROM lookup.countries
+```
+<!--Native-->
+```json
+{
+  "queryType": "scan",
+  "dataSource": {
+    "type": "lookup",
+    "lookup": "countries"
+  },
+  "columns": ["k", "v"],
+  "intervals": ["0000/3000"]
+}
+```
+<!--END_DOCUSAURUS_CODE_TABS-->
+
+Lookup datasources correspond to Druid's key-value [lookup](lookups.html) objects. In [Druid SQL](sql.html#from),
+they reside in the the `lookup` schema. They are preloaded in memory on all servers, so they can be accessed rapidly.
+They can be joined onto regular tables using the [join operator](#join).
+
+Lookup datasources are key-value oriented and always have exactly two columns: `k` (the key) and `v` (the value), and
+both are always strings.
+
+To see a list of all lookup datasources, use the SQL query
+`SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = 'lookup'`.
+
+> Performance tip: Lookups can be joined with a base table either using an explicit [join](#join), or by using the
+> SQL [`LOOKUP` function](sql.html#string-functions).
+> However, the join operator must evaluate the condition on each row, whereas the
+> `LOOKUP` function can defer evaluation until after an aggregation phase. This means that the `LOOKUP` function is
+> usually faster than joining to a lookup datasource.
+
+### `query`
+
+<!--DOCUSAURUS_CODE_TABS-->
+<!--SQL-->
+```sql
+-- Uses a subquery to count hits per page, then takes the average.
+SELECT
+  AVG(cnt) AS average_hits_per_page
+FROM
+  (SELECT page, COUNT(*) AS hits FROM site_traffic GROUP BY page)
+```
+<!--Native-->
+```json
+{
+  "queryType": "timeseries",
+  "dataSource": {
+    "type": "query",
+    "query": {
+      "queryType": "groupBy",
+      "dataSource": "site_traffic",
+      "intervals": ["0000/3000"],
+      "granularity": "all",
+      "dimensions": ["page"],
+      "aggregations": [
+        { "type": "count", "name": "hits" }
+      ]
+    }
+  },
+  "intervals": ["0000/3000"],
+  "granularity": "all",
+  "aggregations": [
+    { "type": "longSum", "name": "hits", "fieldName": "hits" },
+    { "type": "count", "name": "pages" }
+  ],
+  "postAggregations": [
+    { "type": "expression", "name": "average_hits_per_page", "expression": "hits / pages" }
+  ]
+}
+```
+<!--END_DOCUSAURUS_CODE_TABS-->
+
+Query datasources allow you to issue subqueries. In native queries, they can appear anywhere that accepts a
+`dataSource`. In SQL, they can appear in the following places, always surrounded by parentheses:
+
+- The FROM clause: `FROM (<subquery>)`.
+- As inputs to a JOIN: `<table-or-subquery-1> t1 INNER JOIN <table-or-subquery-2> t2 ON t1.<col1> = t2.<col2>`.
+- In the WHERE clause: `WHERE <column> { IN | NOT IN } (<subquery>)`. These are translated to joins by the SQL planner.
+
+> Performance tip: Subquery results need to be fully transferred to the Broker as part of query execution.
+> This means that subqueries with large result sets can cause performance bottlenecks or run into memory usage limits.
+> See the [Query execution](query-execution.md) documentation for more details on how subqueries are executed and what
+> limits will apply.
+
+### `join`
+
+<!--DOCUSAURUS_CODE_TABS-->
+<!--SQL-->
+```sql
+-- Joins "sales" with "countries" (using "store" as the join key) to get sales by country.
+SELECT
+  countries.v AS country,
+  SUM(sales.revenue) AS country_revenue
+FROM
+  sales
+  INNER JOIN lookup.store_to_country ON sales.store = store_to_country.k
+GROUP BY
+  countries.v
+```
+<!--Native-->
 ```json
 {
-	"type": "table",
-	"name": "<string_value>"
+  "queryType": "groupBy",
+  "dataSource": {
+    "type": "join",
+    "left": "sales",
+    "right": {
+      "type": "lookup",
+      "lookup": "store_to_country"
+    },
+    "rightPrefix": "r.",
+    "condition": "store == \"r.k\"",
+    "joinType": "INNER"
+  },
+  "intervals": ["0000/3000"],
+  "granularity": "all",
+  "dimensions": [
+    { "type": "default", "outputName": "country", "dimension": "r.v" }
+  ],
+  "aggregations": [
+    { "type": "longSum", "name": "country_revenue", "fieldName": "revenue" }
+  ]
 }
 ```
+<!--END_DOCUSAURUS_CODE_TABS-->
+
+Join datasources allow you to do a SQL-style join of two datasources. Stacking joins on top of each other allows
+you to join arbitrarily many datasources.
+
+In Druid {{DRUIDVERSION}}, joins are implemented with a broadcast hash-join algorithm. This means that all tables
+other than the leftmost "base" table must fit in memory. It also means that the join condition must be an equality. This
+feature is intended mainly to allow joining regular Druid tables with [lookup](#lookup), [inline](#inline), and
+[query](#query) datasources.
+
+For information about how Druid executes queries involving joins, refer to the
+[Query execution](query-execution.html#join) page.
+
+#### Joins in SQL
+
+SQL joins take the form:
+
+```
+<o1> [ INNER | LEFT [OUTER] ] JOIN <o2> ON <condition>
+```
+
+The condition must involve only equalities, but functions are okay, and there can be multiple equalities ANDed together.
+Conditions like `t1.x = t2.x`, or `LOWER(t1.x) = t2.x`, or `t1.x = t2.x AND t1.y = t2.y` can all be handled. Conditions
+like `t1.x <> t2.x` cannot currently be handled.
 
-### Union datasource
+Note that Druid SQL is less rigid than what native join datasources can handle. In cases where a SQL query does
+something that is not allowed as-is with a native join datasource, Druid SQL will generate a subquery. This can have
+a substantial effect on performance and scalability, so it is something to watch out for. Some examples of when the
+SQL layer will generate subqueries include:
 
-This data source unions two or more table data sources.
+- Joining a regular Druid table to itself, or to another regular Druid table. The native join datasource can accept
+a table on the left-hand side, but not the right, so a subquery is needed.
 
+- Join conditions where the expressions on either side are of different types.
+
+- Join conditions where the right-hand expression is not a direct column access.
+
+For more information about how Druid translates SQL to native queries, refer to the
+[Druid SQL](sql.md#query-translation) documentation.
+
+#### Joins in native queries
+
+Native join datasources have the following properties. All are required.
+
+|Field|Description|
+|-----|-----------|
+|`left`|Left-hand datasource. Must be of type `table`, `join`, `lookup`, `query`, or `inline`. Placing another join as the left datasource allows you to join arbitrarily many datasources.|
+|`right`|Right-hand datasource. Must be of type `lookup`, `query`, or `inline`. Note that this is more rigid than what Druid SQL requires.|
+|`rightPrefix`|String prefix that will be applied to all columns from the right-hand datasource, to prevent them from colliding with columns from the left-hand datasource. Can be any string, so long as it is nonempty and is not be a prefix of the string `__time`. Any columns from the left-hand side that start with your `rightPrefix` will be shadowed. It is up to you to provide a prefix that will not shadow any important columns from the left side.|
+|`condition`|[Expression](../misc/math-expr.md) that must be an equality where one side is an expression of the left-hand side, and the other side is a simple column reference to the right-hand side. Note that this is more rigid than what Druid SQL requires: here, the right-hand reference must be a simple column reference; in SQL it can be an expression.|
+|`joinType`|`INNER` or `LEFT`.|
+
+#### Join performance
+
+Joins are a feature that can significantly affect performance of your queries. Some performance tips and notes:
+
+1. Joins are especially useful with [lookup datasources](#lookup), but in most cases, the
+[`LOOKUP` function](sql.html#string-functions) performs better than a join. Consider using the `LOOKUP` function if
+it is appropriate for your use case.
+2. When using joins in Druid SQL, keep in mind that it can generate subqueries that you did not explicitly include in
+your queries. Refer to the [Druid SQL](sql.md#query-translation) documentation for more details about when this happens
+and how to detect it.
+3. One common reason for implicit subquery generation is if the types of the two halves of an equality do not match.
+For example, since lookup keys are always strings, the condition `druid.d JOIN lookup.l ON d.field = l.field` will
+perform best if `d.field` is a string.
+4. As of Druid {{DRUIDVERSION}}, the join operator must evaluate the condition for each row. In the future, we expect
+to implement both early and deferred condition evaluation, which we expect to improve performance considerably for
+common use cases.
+
+#### Future work for joins
+
+Joins are an area of active development in Druid. The following features are missing today but may appear in
+future versions:
+
+- Preloaded dimension tables that are wider than lookups (i.e. supporting more than a single key and single value).
+- RIGHT OUTER and FULL OUTER joins. Currently, they are partially implemented. Queries will run but results will not
+always be correct.
+- Performance-related optimizations as mentioned in the [previous section](#join-performance).
+- Join algorithms other than broadcast hash-joins.
+
+### `union`
+
+<!--DOCUSAURUS_CODE_TABS-->
+<!--Native-->
 ```json
 {
-       "type": "union",
-       "dataSources": ["<string_value1>", "<string_value2>", "<string_value3>", ... ]
+  "queryType": "scan",
+  "dataSource": {
+    "type": "union",
+    "dataSources": ["<dataSourceName1>", "<dataSourceName2>", "<dataSourceName3>"]
+  },
+  "columns": ["column1", "column2"],
+  "intervals": ["0000/3000"]
 }
 ```
+<!--END_DOCUSAURUS_CODE_TABS-->
 
-Note that the data sources being unioned should have the same schema.
-Union Queries should be always sent to a Broker/Router process and are *NOT* supported directly by the Historical processes.
+Union datasources allow you to treat two or more table datasources as a single datasource. The datasources being unioned
+do not need to have identical schemas. If they do not fully match up, then columns that exist in one table but not
+another will be treated as if they contained all null values in the tables where they do not exist.
 
 Review comment:
   It is indirectly described [here](https://github.com/apache/druid/pull/9704/files#diff-203b8f56b37fff44bf7ab3b38b94308bR105-R107). 

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