mirror of https://github.com/apache/druid.git
MSQ: Validate that strings and string arrays are not mixed. (#15920)
* MSQ: Validate that strings and string arrays are not mixed. When multi-value strings and string arrays coexist in the same column, it causes problems with "classic MVD" style queries such as: select * from wikipedia -- fails at runtime select count(*) from wikipedia where flags = 'B' -- fails at planning time select flags, count(*) from wikipedia group by 1 -- fails at runtime To avoid these problems, this patch adds type verification for INSERT and REPLACE. It is targeted: the only type changes that are blocked are string-to-array and array-to-string. There is also a way to exclude certain columns from the type checks, if the user really knows what they're doing. * Fixes. * Tests and docs and error messages. * More docs. * Adjustments. * Adjust message. * Fix tests. * Fix test in DV mode.
This commit is contained in:
Gian Merlino
GitHub
parent
ccae19a546
commit
256160aba6
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@ -200,8 +200,8 @@ To perform ingestion with rollup:
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2. Set [`finalizeAggregations: false`](reference.md#context-parameters) in your context. This causes aggregation
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functions to write their internal state to the generated segments, instead of the finalized end result, and enables
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further aggregation at query time.
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3. See [ARRAY types](../querying/arrays.md#sql-based-ingestion-with-rollup) for information about ingesting `ARRAY` columns
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4. See [multi-value dimensions](../querying/multi-value-dimensions.md#sql-based-ingestion-with-rollup) for information to ingest multi-value VARCHAR columns
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3. See [ARRAY types](../querying/arrays.md#sql-based-ingestion) for information about ingesting `ARRAY` columns
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4. See [multi-value dimensions](../querying/multi-value-dimensions.md#sql-based-ingestion) for information to ingest multi-value VARCHAR columns
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When you do all of these things, Druid understands that you intend to do an ingestion with rollup, and it writes
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rollup-related metadata into the generated segments. Other applications can then use [`segmentMetadata`
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@ -351,7 +351,7 @@ The following table lists the context parameters for the MSQ task engine:
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| `maxNumTasks` | SELECT, INSERT, REPLACE<br /><br />The maximum total number of tasks to launch, including the controller task. The lowest possible value for this setting is 2: one controller and one worker. All tasks must be able to launch simultaneously. If they cannot, the query returns a `TaskStartTimeout` error code after approximately 10 minutes.<br /><br />May also be provided as `numTasks`. If both are present, `maxNumTasks` takes priority. | 2 |
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| `taskAssignment` | SELECT, INSERT, REPLACE<br /><br />Determines how many tasks to use. Possible values include: <ul><li>`max`: Uses as many tasks as possible, up to `maxNumTasks`.</li><li>`auto`: When file sizes can be determined through directory listing (for example: local files, S3, GCS, HDFS) uses as few tasks as possible without exceeding 512 MiB or 10,000 files per task, unless exceeding these limits is necessary to stay within `maxNumTasks`. When calculating the size of files, the weighted size is used, which considers the file format and compression format used if any. When file sizes cannot be determined through directory listing (for example: http), behaves the same as `max`.</li></ul> | `max` |
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| `finalizeAggregations` | SELECT, INSERT, REPLACE<br /><br />Determines the type of aggregation to return. If true, Druid finalizes the results of complex aggregations that directly appear in query results. If false, Druid returns the aggregation's intermediate type rather than finalized type. This parameter is useful during ingestion, where it enables storing sketches directly in Druid tables. For more information about aggregations, see [SQL aggregation functions](../querying/sql-aggregations.md). | `true` |
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| `arrayIngestMode` | INSERT, REPLACE<br /><br /> Controls how ARRAY type values are stored in Druid segments. When set to `array` (recommended for SQL compliance), Druid will store all ARRAY typed values in [ARRAY typed columns](../querying/arrays.md), and supports storing both VARCHAR and numeric typed arrays. When set to `mvd` (the default, for backwards compatibility), Druid only supports VARCHAR typed arrays, and will store them as [multi-value string columns](../querying/multi-value-dimensions.md). When set to `none`, Druid will throw an exception when trying to store any type of arrays. `none` is most useful when set in the system default query context with (`druid.query.default.context.arrayIngestMode=none`) to be used to help migrate operators from `mvd` mode to `array` mode and force query writers to make an explicit choice between ARRAY and multi-value VARCHAR typed columns. | `mvd` (for backwards compatibility, recommended to use `array` for SQL compliance)|
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| `arrayIngestMode` | INSERT, REPLACE<br /><br /> Controls how ARRAY type values are stored in Druid segments. When set to `array` (recommended for SQL compliance), Druid will store all ARRAY typed values in [ARRAY typed columns](../querying/arrays.md), and supports storing both VARCHAR and numeric typed arrays. When set to `mvd` (the default, for backwards compatibility), Druid only supports VARCHAR typed arrays, and will store them as [multi-value string columns](../querying/multi-value-dimensions.md). See [`arrayIngestMode`] in the [Arrays](../querying/arrays.md) page for more details. | `mvd` (for backwards compatibility, recommended to use `array` for SQL compliance)|
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| `sqlJoinAlgorithm` | SELECT, INSERT, REPLACE<br /><br />Algorithm to use for JOIN. Use `broadcast` (the default) for broadcast hash join or `sortMerge` for sort-merge join. Affects all JOIN operations in the query. This is a hint to the MSQ engine and the actual joins in the query may proceed in a different way than specified. See [Joins](#joins) for more details. | `broadcast` |
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| `rowsInMemory` | INSERT or REPLACE<br /><br />Maximum number of rows to store in memory at once before flushing to disk during the segment generation process. Ignored for non-INSERT queries. In most cases, use the default value. You may need to override the default if you run into one of the [known issues](./known-issues.md) around memory usage. | 100,000 |
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| `segmentSortOrder` | INSERT or REPLACE<br /><br />Normally, Druid sorts rows in individual segments using `__time` first, followed by the [CLUSTERED BY](#clustered-by) clause. When you set `segmentSortOrder`, Druid sorts rows in segments using this column list first, followed by the CLUSTERED BY order.<br /><br />You provide the column list as comma-separated values or as a JSON array in string form. If your query includes `__time`, then this list must begin with `__time`. For example, consider an INSERT query that uses `CLUSTERED BY country` and has `segmentSortOrder` set to `__time,city`. Within each time chunk, Druid assigns rows to segments based on `country`, and then within each of those segments, Druid sorts those rows by `__time` first, then `city`, then `country`. | empty list |
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@ -364,6 +364,7 @@ The following table lists the context parameters for the MSQ task engine:
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| `waitUntilSegmentsLoad` | INSERT, REPLACE<br /><br /> If set, the ingest query waits for the generated segment to be loaded before exiting, else the ingest query exits without waiting. The task and live reports contain the information about the status of loading segments if this flag is set. This will ensure that any future queries made after the ingestion exits will include results from the ingestion. The drawback is that the controller task will stall till the segments are loaded. | `false` |
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| `includeSegmentSource` | SELECT, INSERT, REPLACE<br /><br /> Controls the sources, which will be queried for results in addition to the segments present on deep storage. Can be `NONE` or `REALTIME`. If this value is `NONE`, only non-realtime (published and used) segments will be downloaded from deep storage. If this value is `REALTIME`, results will also be included from realtime tasks. | `NONE` |
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| `rowsPerPage` | SELECT<br /><br />The number of rows per page to target. The actual number of rows per page may be somewhat higher or lower than this number. In most cases, use the default.<br /> This property comes into effect only when `selectDestination` is set to `durableStorage` | 100000 |
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| `skipTypeVerification` | INSERT or REPLACE<br /><br />During query validation, Druid validates that [string arrays](../querying/arrays.md) and [multi-value dimensions](../querying/multi-value-dimensions.md) are not mixed in the same column. If you are intentionally migrating from one to the other, use this context parameter to disable type validation.<br /><br />Provide the column list as comma-separated values or as a JSON array in string form.| empty list |
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| `failOnEmptyInsert` | INSERT or REPLACE<br /><br /> When set to false (the default), an INSERT query generating no output rows will be no-op, and a REPLACE query generating no output rows will delete all data that matches the OVERWRITE clause. When set to true, an ingest query generating no output rows will throw an `InsertCannotBeEmpty` fault. | `false` |
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## Joins
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@ -71,9 +71,46 @@ The following shows an example `dimensionsSpec` for native ingestion of the data
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### SQL-based ingestion
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Arrays can also be inserted with [SQL-based ingestion](../multi-stage-query/index.md) when you include a query context parameter [`"arrayIngestMode":"array"`](../multi-stage-query/reference.md#context-parameters).
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#### `arrayIngestMode`
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Arrays can be inserted with [SQL-based ingestion](../multi-stage-query/index.md) when you include the query context
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parameter `arrayIngestMode: array`.
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When `arrayIngestMode` is `array`, SQL ARRAY types are stored using Druid array columns. This is recommended for new
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tables.
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When `arrayIngestMode` is `mvd`, SQL `VARCHAR ARRAY` are implicitly wrapped in [`ARRAY_TO_MV`](sql-functions.md#array_to_mv).
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This causes them to be stored as [multi-value strings](multi-value-dimensions.md), using the same `STRING` column type
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as regular scalar strings. SQL `BIGINT ARRAY` and `DOUBLE ARRAY` cannot be loaded under `arrayIngestMode: mvd`. This
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is the default behavior when `arrayIngestMode` is not provided in your query context, although the default behavior
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may change to `array` in a future release.
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When `arrayIngestMode` is `none`, Druid throws an exception when trying to store any type of arrays. This mode is most
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useful when set in the system default query context with `druid.query.default.context.arrayIngestMode = none`, in cases
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where the cluster administrator wants SQL query authors to explicitly provide one or the other in their query context.
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The following table summarizes the differences in SQL ARRAY handling between `arrayIngestMode: array` and
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`arrayIngestMode: mvd`.
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| SQL type | Stored type when `arrayIngestMode: array` | Stored type when `arrayIngestMode: mvd` (default) |
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|---|---|---|
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|`VARCHAR ARRAY`|`ARRAY<STRING>`|[multi-value `STRING`](multi-value-dimensions.md)|
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|`BIGINT ARRAY`|`ARRAY<LONG>`|not possible (validation error)|
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|`DOUBLE ARRAY`|`ARRAY<DOUBLE>`|not possible (validation error)|
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In either mode, you can explicitly wrap string arrays in `ARRAY_TO_MV` to cause them to be stored as
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[multi-value strings](multi-value-dimensions.md).
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When validating a SQL INSERT or REPLACE statement that contains arrays, Druid checks whether the statement would lead
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to mixing string arrays and multi-value strings in the same column. If this condition is detected, the statement fails
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validation unless the column is named under the `skipTypeVerification` context parameter. This parameter can be either
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a comma-separated list of column names, or a JSON array in string form. This validation is done to prevent accidentally
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mixing arrays and multi-value strings in the same column.
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#### Examples
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Set [`arrayIngestMode: array`](#arrayingestmode) in your query context to run the following examples.
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For example, to insert the data used in this document:
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```sql
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REPLACE INTO "array_example" OVERWRITE ALL
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WITH "ext" AS (
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@ -81,9 +118,14 @@ WITH "ext" AS (
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FROM TABLE(
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EXTERN(
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'{"type":"inline","data":"{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row1\", \"arrayString\": [\"a\", \"b\"], \"arrayLong\":[1, null,3], \"arrayDouble\":[1.1, 2.2, null]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row2\", \"arrayString\": [null, \"b\"], \"arrayLong\":null, \"arrayDouble\":[999, null, 5.5]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row3\", \"arrayString\": [], \"arrayLong\":[1, 2, 3], \"arrayDouble\":[null, 2.2, 1.1]} \n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row4\", \"arrayString\": [\"a\", \"b\"], \"arrayLong\":[1, 2, 3], \"arrayDouble\":[]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row5\", \"arrayString\": null, \"arrayLong\":[], \"arrayDouble\":null}"}',
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'{"type":"json"}',
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'[{"name":"timestamp", "type":"STRING"},{"name":"label", "type":"STRING"},{"name":"arrayString", "type":"ARRAY<STRING>"},{"name":"arrayLong", "type":"ARRAY<LONG>"},{"name":"arrayDouble", "type":"ARRAY<DOUBLE>"}]'
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'{"type":"json"}'
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)
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) EXTEND (
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"timestamp" VARCHAR,
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"label" VARCHAR,
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"arrayString" VARCHAR ARRAY,
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"arrayLong" BIGINT ARRAY,
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"arrayDouble" DOUBLE ARRAY
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)
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)
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SELECT
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@ -96,8 +138,7 @@ FROM "ext"
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PARTITIONED BY DAY
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```
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### SQL-based ingestion with rollup
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These input arrays can also be grouped for rollup:
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Arrays can also be used as `GROUP BY` keys for rollup:
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```sql
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REPLACE INTO "array_example_rollup" OVERWRITE ALL
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@ -106,9 +147,14 @@ WITH "ext" AS (
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FROM TABLE(
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EXTERN(
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'{"type":"inline","data":"{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row1\", \"arrayString\": [\"a\", \"b\"], \"arrayLong\":[1, null,3], \"arrayDouble\":[1.1, 2.2, null]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row2\", \"arrayString\": [null, \"b\"], \"arrayLong\":null, \"arrayDouble\":[999, null, 5.5]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row3\", \"arrayString\": [], \"arrayLong\":[1, 2, 3], \"arrayDouble\":[null, 2.2, 1.1]} \n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row4\", \"arrayString\": [\"a\", \"b\"], \"arrayLong\":[1, 2, 3], \"arrayDouble\":[]}\n{\"timestamp\": \"2023-01-01T00:00:00\", \"label\": \"row5\", \"arrayString\": null, \"arrayLong\":[], \"arrayDouble\":null}"}',
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'{"type":"json"}',
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'[{"name":"timestamp", "type":"STRING"},{"name":"label", "type":"STRING"},{"name":"arrayString", "type":"ARRAY<STRING>"},{"name":"arrayLong", "type":"ARRAY<LONG>"},{"name":"arrayDouble", "type":"ARRAY<DOUBLE>"}]'
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'{"type":"json"}'
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)
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) EXTEND (
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"timestamp" VARCHAR,
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"label" VARCHAR,
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"arrayString" VARCHAR ARRAY,
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"arrayLong" BIGINT ARRAY,
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"arrayDouble" DOUBLE ARRAY
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)
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)
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SELECT
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@ -507,9 +507,9 @@ Avoid confusing string arrays with [multi-value dimensions](multi-value-dimensio
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Use care during ingestion to ensure you get the type you want.
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To get arrays when performing an ingestion using JSON ingestion specs, such as [native batch](../ingestion/native-batch.md) or streaming ingestion such as with [Apache Kafka](../ingestion/kafka-ingestion.md), use dimension type `auto` or enable `useSchemaDiscovery`. When performing a [SQL-based ingestion](../multi-stage-query/index.md), write a query that generates arrays and set the context parameter `"arrayIngestMode": "array"`. Arrays may contain strings or numbers.
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To get arrays when performing an ingestion using JSON ingestion specs, such as [native batch](../ingestion/native-batch.md) or streaming ingestion such as with [Apache Kafka](../ingestion/kafka-ingestion.md), use dimension type `auto` or enable `useSchemaDiscovery`. When performing a [SQL-based ingestion](../multi-stage-query/index.md), write a query that generates arrays and set the context parameter [`"arrayIngestMode": "array"`](arrays.md#arrayingestmode). Arrays may contain strings or numbers.
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To get multi-value dimensions when performing an ingestion using JSON ingestion specs, use dimension type `string` and do not enable `useSchemaDiscovery`. When performing a [SQL-based ingestion](../multi-stage-query/index.md), wrap arrays in [`ARRAY_TO_MV`](multi-value-dimensions.md#sql-based-ingestion), which ensures you get multi-value dimensions in any `arrayIngestMode`. Multi-value dimensions can only contain strings.
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To get multi-value dimensions when performing an ingestion using JSON ingestion specs, use dimension type `string` and do not enable `useSchemaDiscovery`. When performing a [SQL-based ingestion](../multi-stage-query/index.md), wrap arrays in [`ARRAY_TO_MV`](multi-value-dimensions.md#sql-based-ingestion), which ensures you get multi-value dimensions in any [`arrayIngestMode`](arrays.md#arrayingestmode). Multi-value dimensions can only contain strings.
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You can tell which type you have by checking the `INFORMATION_SCHEMA.COLUMNS` table, using a query like:
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@ -2135,9 +2135,13 @@ public class ControllerImpl implements Controller
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// deprecation and removal in future
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if (MultiStageQueryContext.getArrayIngestMode(query.context()) == ArrayIngestMode.MVD) {
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log.warn(
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"'%s' is set to 'mvd' in the query's context. This ingests the string arrays as multi-value "
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+ "strings instead of arrays, and is preserved for legacy reasons when MVDs were the only way to ingest string "
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+ "arrays in Druid. It is incorrect behaviour and will likely be removed in the future releases of Druid",
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"%s[mvd] is active for this task. This causes string arrays (VARCHAR ARRAY in SQL) to be ingested as "
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+ "multi-value strings rather than true arrays. This behavior may change in a future version of Druid. To be "
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+ "compatible with future behavior changes, we recommend setting %s to[array], which creates a clearer "
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+ "separation between multi-value strings and true arrays. In either[mvd] or[array] mode, you can write "
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+ "out multi-value string dimensions using ARRAY_TO_MV. "
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+ "See https://druid.apache.org/docs/latest/querying/arrays#arrayingestmode for more details.",
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MultiStageQueryContext.CTX_ARRAY_INGEST_MODE,
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MultiStageQueryContext.CTX_ARRAY_INGEST_MODE
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);
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}
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@ -29,20 +29,29 @@ import org.apache.calcite.rel.core.Project;
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import org.apache.calcite.rel.core.Sort;
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import org.apache.calcite.rel.type.RelDataType;
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import org.apache.calcite.rel.type.RelDataTypeFactory;
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import org.apache.calcite.rel.type.RelDataTypeField;
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import org.apache.calcite.schema.Table;
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import org.apache.calcite.sql.dialect.CalciteSqlDialect;
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import org.apache.calcite.sql.type.SqlTypeName;
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import org.apache.calcite.util.Pair;
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import org.apache.druid.error.DruidException;
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import org.apache.druid.error.InvalidInput;
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import org.apache.druid.error.InvalidSqlInput;
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import org.apache.druid.java.util.common.StringUtils;
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import org.apache.druid.java.util.common.granularity.Granularities;
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import org.apache.druid.java.util.common.granularity.Granularity;
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import org.apache.druid.msq.querykit.QueryKitUtils;
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import org.apache.druid.msq.util.ArrayIngestMode;
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import org.apache.druid.msq.util.DimensionSchemaUtils;
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import org.apache.druid.msq.util.MultiStageQueryContext;
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import org.apache.druid.rpc.indexing.OverlordClient;
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import org.apache.druid.segment.column.ColumnHolder;
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import org.apache.druid.segment.column.ColumnType;
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import org.apache.druid.segment.column.ValueType;
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import org.apache.druid.sql.calcite.parser.DruidSqlIngest;
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import org.apache.druid.sql.calcite.parser.DruidSqlInsert;
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import org.apache.druid.sql.calcite.planner.Calcites;
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import org.apache.druid.sql.calcite.planner.DruidTypeSystem;
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import org.apache.druid.sql.calcite.planner.PlannerContext;
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import org.apache.druid.sql.calcite.run.EngineFeature;
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import org.apache.druid.sql.calcite.run.NativeSqlEngine;
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@ -50,7 +59,9 @@ import org.apache.druid.sql.calcite.run.QueryMaker;
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import org.apache.druid.sql.calcite.run.SqlEngine;
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import org.apache.druid.sql.calcite.run.SqlEngines;
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import org.apache.druid.sql.destination.IngestDestination;
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import org.apache.druid.sql.destination.TableDestination;
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import javax.annotation.Nullable;
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import java.util.HashSet;
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import java.util.List;
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import java.util.Map;
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@ -163,7 +174,18 @@ public class MSQTaskSqlEngine implements SqlEngine
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final PlannerContext plannerContext
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)
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{
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validateInsert(relRoot.rel, relRoot.fields, plannerContext);
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validateInsert(
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relRoot.rel,
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relRoot.fields,
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destination instanceof TableDestination
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? plannerContext.getPlannerToolbox()
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.rootSchema()
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.getNamedSchema(plannerContext.getPlannerToolbox().druidSchemaName())
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.getSchema()
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.getTable(((TableDestination) destination).getTableName())
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: null,
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plannerContext
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);
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return new MSQTaskQueryMaker(
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destination,
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@ -194,71 +216,24 @@ public class MSQTaskSqlEngine implements SqlEngine
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}
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}
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/**
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* Engine-specific validation that happens after the query is planned.
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*/
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private static void validateInsert(
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final RelNode rootRel,
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final List<Pair<Integer, String>> fieldMappings,
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@Nullable Table targetTable,
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final PlannerContext plannerContext
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)
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{
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final int timeColumnIndex = getTimeColumnIndex(fieldMappings);
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final Granularity segmentGranularity = getSegmentGranularity(plannerContext);
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validateNoDuplicateAliases(fieldMappings);
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// Find the __time field.
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int timeFieldIndex = -1;
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for (final Pair<Integer, String> field : fieldMappings) {
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if (field.right.equals(ColumnHolder.TIME_COLUMN_NAME)) {
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timeFieldIndex = field.left;
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// Validate the __time field has the proper type.
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final SqlTypeName timeType = rootRel.getRowType().getFieldList().get(field.left).getType().getSqlTypeName();
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if (timeType != SqlTypeName.TIMESTAMP) {
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throw InvalidSqlInput.exception(
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"Field [%s] was the wrong type [%s], expected TIMESTAMP",
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ColumnHolder.TIME_COLUMN_NAME,
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timeType
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);
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}
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}
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}
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|
||||
// Validate that if segmentGranularity is not ALL then there is also a __time field.
|
||||
final Granularity segmentGranularity;
|
||||
|
||||
try {
|
||||
segmentGranularity = QueryKitUtils.getSegmentGranularityFromContext(
|
||||
plannerContext.getJsonMapper(),
|
||||
plannerContext.queryContextMap()
|
||||
);
|
||||
}
|
||||
catch (Exception e) {
|
||||
// This is a defensive check as the DruidSqlInsert.SQL_INSERT_SEGMENT_GRANULARITY in the query context is
|
||||
// populated by Druid. If the user entered an incorrect granularity, that should have been flagged before reaching
|
||||
// here
|
||||
throw DruidException.forPersona(DruidException.Persona.DEVELOPER)
|
||||
.ofCategory(DruidException.Category.DEFENSIVE)
|
||||
.build(
|
||||
e,
|
||||
"[%s] is not a valid value for [%s]",
|
||||
plannerContext.queryContext().get(DruidSqlInsert.SQL_INSERT_SEGMENT_GRANULARITY),
|
||||
DruidSqlInsert.SQL_INSERT_SEGMENT_GRANULARITY
|
||||
);
|
||||
|
||||
}
|
||||
|
||||
final boolean hasSegmentGranularity = !Granularities.ALL.equals(segmentGranularity);
|
||||
|
||||
// Validate that the query does not have an inappropriate LIMIT or OFFSET. LIMIT prevents gathering result key
|
||||
// statistics, which INSERT execution logic depends on. (In QueryKit, LIMIT disables statistics generation and
|
||||
// funnels everything through a single partition.)
|
||||
validateLimitAndOffset(rootRel, !hasSegmentGranularity);
|
||||
|
||||
if (hasSegmentGranularity && timeFieldIndex < 0) {
|
||||
throw InvalidInput.exception(
|
||||
"The granularity [%s] specified in the PARTITIONED BY clause of the INSERT query is different from ALL. "
|
||||
+ "Therefore, the query must specify a time column (named __time).",
|
||||
segmentGranularity
|
||||
);
|
||||
}
|
||||
validateTimeColumnType(rootRel, timeColumnIndex);
|
||||
validateTimeColumnExistsIfNeeded(timeColumnIndex, segmentGranularity);
|
||||
validateLimitAndOffset(rootRel, Granularities.ALL.equals(segmentGranularity));
|
||||
validateTypeChanges(rootRel, fieldMappings, targetTable, plannerContext);
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -276,15 +251,70 @@ public class MSQTaskSqlEngine implements SqlEngine
|
|||
}
|
||||
}
|
||||
|
||||
private static void validateLimitAndOffset(final RelNode topRel, final boolean limitOk)
|
||||
/**
|
||||
* Validate the time field {@link ColumnHolder#TIME_COLUMN_NAME} has type TIMESTAMP.
|
||||
*
|
||||
* @param rootRel root rel
|
||||
* @param timeColumnIndex index of the time field
|
||||
*/
|
||||
private static void validateTimeColumnType(final RelNode rootRel, final int timeColumnIndex)
|
||||
{
|
||||
if (timeColumnIndex < 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
// Validate the __time field has the proper type.
|
||||
final SqlTypeName timeType = rootRel.getRowType().getFieldList().get(timeColumnIndex).getType().getSqlTypeName();
|
||||
if (timeType != SqlTypeName.TIMESTAMP) {
|
||||
throw InvalidSqlInput.exception(
|
||||
"Field[%s] was the wrong type[%s], expected TIMESTAMP",
|
||||
ColumnHolder.TIME_COLUMN_NAME,
|
||||
timeType
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Validate that if segmentGranularity is not ALL, then there is also a {@link ColumnHolder#TIME_COLUMN_NAME} field.
|
||||
*
|
||||
* @param segmentGranularity granularity from {@link #getSegmentGranularity(PlannerContext)}
|
||||
* @param timeColumnIndex index of the time field
|
||||
*/
|
||||
private static void validateTimeColumnExistsIfNeeded(
|
||||
final int timeColumnIndex,
|
||||
final Granularity segmentGranularity
|
||||
)
|
||||
{
|
||||
final boolean hasSegmentGranularity = !Granularities.ALL.equals(segmentGranularity);
|
||||
|
||||
if (hasSegmentGranularity && timeColumnIndex < 0) {
|
||||
throw InvalidInput.exception(
|
||||
"The granularity [%s] specified in the PARTITIONED BY clause of the INSERT query is different from ALL. "
|
||||
+ "Therefore, the query must specify a time column (named __time).",
|
||||
segmentGranularity
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Validate that the query does not have an inappropriate LIMIT or OFFSET. LIMIT prevents gathering result key
|
||||
* statistics, which INSERT execution logic depends on. (In QueryKit, LIMIT disables statistics generation and
|
||||
* funnels everything through a single partition.)
|
||||
*
|
||||
* LIMIT is allowed when segment granularity is ALL, disallowed otherwise. OFFSET is never allowed.
|
||||
*
|
||||
* @param rootRel root rel
|
||||
* @param limitOk whether LIMIT is ok (OFFSET is never ok)
|
||||
*/
|
||||
private static void validateLimitAndOffset(final RelNode rootRel, final boolean limitOk)
|
||||
{
|
||||
Sort sort = null;
|
||||
|
||||
if (topRel instanceof Sort) {
|
||||
sort = (Sort) topRel;
|
||||
} else if (topRel instanceof Project) {
|
||||
if (rootRel instanceof Sort) {
|
||||
sort = (Sort) rootRel;
|
||||
} else if (rootRel instanceof Project) {
|
||||
// Look for Project after a Sort, then validate the sort.
|
||||
final Project project = (Project) topRel;
|
||||
final Project project = (Project) rootRel;
|
||||
if (project.isMapping()) {
|
||||
final RelNode projectInput = project.getInput();
|
||||
if (projectInput instanceof Sort) {
|
||||
|
|
@ -308,6 +338,132 @@ public class MSQTaskSqlEngine implements SqlEngine
|
|||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Validate that the query does not include any type changes from string to array or vice versa.
|
||||
*
|
||||
* These type changes tend to cause problems due to mixing of multi-value strings and string arrays. In particular,
|
||||
* many queries written in the "classic MVD" style (treating MVDs as if they were regular strings) will fail when
|
||||
* MVDs and arrays are mixed. So, we detect them as invalid.
|
||||
*
|
||||
* @param rootRel root rel
|
||||
* @param fieldMappings field mappings from {@link #validateInsert(RelNode, List, Table, PlannerContext)}
|
||||
* @param targetTable table we are inserting (or replacing) into, if any
|
||||
* @param plannerContext planner context
|
||||
*/
|
||||
private static void validateTypeChanges(
|
||||
final RelNode rootRel,
|
||||
final List<Pair<Integer, String>> fieldMappings,
|
||||
@Nullable final Table targetTable,
|
||||
final PlannerContext plannerContext
|
||||
)
|
||||
{
|
||||
if (targetTable == null) {
|
||||
return;
|
||||
}
|
||||
|
||||
final Set<String> columnsExcludedFromTypeVerification =
|
||||
MultiStageQueryContext.getColumnsExcludedFromTypeVerification(plannerContext.queryContext());
|
||||
final ArrayIngestMode arrayIngestMode = MultiStageQueryContext.getArrayIngestMode(plannerContext.queryContext());
|
||||
|
||||
for (Pair<Integer, String> fieldMapping : fieldMappings) {
|
||||
final int columnIndex = fieldMapping.left;
|
||||
final String columnName = fieldMapping.right;
|
||||
final RelDataTypeField oldSqlTypeField =
|
||||
targetTable.getRowType(DruidTypeSystem.TYPE_FACTORY).getField(columnName, true, false);
|
||||
|
||||
if (!columnsExcludedFromTypeVerification.contains(columnName) && oldSqlTypeField != null) {
|
||||
final ColumnType oldDruidType = Calcites.getColumnTypeForRelDataType(oldSqlTypeField.getType());
|
||||
final RelDataType newSqlType = rootRel.getRowType().getFieldList().get(columnIndex).getType();
|
||||
final ColumnType newDruidType =
|
||||
DimensionSchemaUtils.getDimensionType(Calcites.getColumnTypeForRelDataType(newSqlType), arrayIngestMode);
|
||||
|
||||
if (newDruidType.isArray() && oldDruidType.is(ValueType.STRING)
|
||||
|| (newDruidType.is(ValueType.STRING) && oldDruidType.isArray())) {
|
||||
final StringBuilder messageBuilder = new StringBuilder(
|
||||
StringUtils.format(
|
||||
"Cannot write into field[%s] using type[%s] and arrayIngestMode[%s], since the existing type is[%s]",
|
||||
columnName,
|
||||
newSqlType,
|
||||
StringUtils.toLowerCase(arrayIngestMode.toString()),
|
||||
oldSqlTypeField.getType()
|
||||
)
|
||||
);
|
||||
|
||||
if (newDruidType.is(ValueType.STRING)
|
||||
&& newSqlType.getSqlTypeName() == SqlTypeName.ARRAY
|
||||
&& arrayIngestMode == ArrayIngestMode.MVD) {
|
||||
// Tried to insert a SQL ARRAY, which got turned into a STRING by arrayIngestMode: mvd.
|
||||
messageBuilder.append(". Try setting arrayIngestMode to[array] to retain the SQL type[")
|
||||
.append(newSqlType)
|
||||
.append("]");
|
||||
} else if (newDruidType.is(ValueType.ARRAY)
|
||||
&& oldDruidType.is(ValueType.STRING)
|
||||
&& arrayIngestMode == ArrayIngestMode.ARRAY) {
|
||||
// Tried to insert a SQL ARRAY, which stayed an ARRAY, but wasn't compatible with existing STRING.
|
||||
messageBuilder.append(". Try wrapping this field using ARRAY_TO_MV(...) AS ")
|
||||
.append(CalciteSqlDialect.DEFAULT.quoteIdentifier(columnName));
|
||||
} else if (newDruidType.is(ValueType.STRING) && oldDruidType.is(ValueType.ARRAY)) {
|
||||
// Tried to insert a SQL VARCHAR, but wasn't compatible with existing ARRAY.
|
||||
messageBuilder.append(". Try");
|
||||
if (arrayIngestMode == ArrayIngestMode.MVD) {
|
||||
messageBuilder.append(" setting arrayIngestMode to[array] and");
|
||||
}
|
||||
messageBuilder.append(" adjusting your query to make this column an ARRAY instead of VARCHAR");
|
||||
}
|
||||
|
||||
messageBuilder.append(". See https://druid.apache.org/docs/latest/querying/arrays#arrayingestmode "
|
||||
+ "for more details about this check and how to override it if needed.");
|
||||
|
||||
throw InvalidSqlInput.exception(StringUtils.encodeForFormat(messageBuilder.toString()));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Returns the index of {@link ColumnHolder#TIME_COLUMN_NAME} within a list of field mappings from
|
||||
* {@link #validateInsert(RelNode, List, Table, PlannerContext)}.
|
||||
*
|
||||
* Returns -1 if the list does not contain a time column.
|
||||
*/
|
||||
private static int getTimeColumnIndex(final List<Pair<Integer, String>> fieldMappings)
|
||||
{
|
||||
for (final Pair<Integer, String> field : fieldMappings) {
|
||||
if (field.right.equals(ColumnHolder.TIME_COLUMN_NAME)) {
|
||||
return field.left;
|
||||
}
|
||||
}
|
||||
|
||||
return -1;
|
||||
}
|
||||
|
||||
/**
|
||||
* Retrieve the segment granularity for a query.
|
||||
*/
|
||||
private static Granularity getSegmentGranularity(final PlannerContext plannerContext)
|
||||
{
|
||||
try {
|
||||
return QueryKitUtils.getSegmentGranularityFromContext(
|
||||
plannerContext.getJsonMapper(),
|
||||
plannerContext.queryContextMap()
|
||||
);
|
||||
}
|
||||
catch (Exception e) {
|
||||
// This is a defensive check as the DruidSqlInsert.SQL_INSERT_SEGMENT_GRANULARITY in the query context is
|
||||
// populated by Druid. If the user entered an incorrect granularity, that should have been flagged before reaching
|
||||
// here.
|
||||
throw DruidException.forPersona(DruidException.Persona.DEVELOPER)
|
||||
.ofCategory(DruidException.Category.DEFENSIVE)
|
||||
.build(
|
||||
e,
|
||||
"[%s] is not a valid value for [%s]",
|
||||
plannerContext.queryContext().get(DruidSqlInsert.SQL_INSERT_SEGMENT_GRANULARITY),
|
||||
DruidSqlInsert.SQL_INSERT_SEGMENT_GRANULARITY
|
||||
);
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
private static RelDataType getMSQStructType(RelDataTypeFactory typeFactory)
|
||||
{
|
||||
return typeFactory.createStructType(
|
||||
|
|
|
|||
|
|
@ -57,9 +57,19 @@ public class DimensionSchemaUtils
|
|||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a dimension schema for a dimension column, given the type that it was assigned in the query, and the
|
||||
* current values of {@link MultiStageQueryContext#CTX_USE_AUTO_SCHEMAS} and
|
||||
* {@link MultiStageQueryContext#CTX_ARRAY_INGEST_MODE}.
|
||||
*
|
||||
* @param column column name
|
||||
* @param queryType type of the column from the query
|
||||
* @param useAutoType active value of {@link MultiStageQueryContext#CTX_USE_AUTO_SCHEMAS}
|
||||
* @param arrayIngestMode active value of {@link MultiStageQueryContext#CTX_ARRAY_INGEST_MODE}
|
||||
*/
|
||||
public static DimensionSchema createDimensionSchema(
|
||||
final String column,
|
||||
@Nullable final ColumnType type,
|
||||
@Nullable final ColumnType queryType,
|
||||
boolean useAutoType,
|
||||
ArrayIngestMode arrayIngestMode
|
||||
)
|
||||
|
|
@ -67,66 +77,92 @@ public class DimensionSchemaUtils
|
|||
if (useAutoType) {
|
||||
// for complex types that are not COMPLEX<json>, we still want to use the handler since 'auto' typing
|
||||
// only works for the 'standard' built-in types
|
||||
if (type != null && type.is(ValueType.COMPLEX) && !ColumnType.NESTED_DATA.equals(type)) {
|
||||
final ColumnCapabilities capabilities = ColumnCapabilitiesImpl.createDefault().setType(type);
|
||||
if (queryType != null && queryType.is(ValueType.COMPLEX) && !ColumnType.NESTED_DATA.equals(queryType)) {
|
||||
final ColumnCapabilities capabilities = ColumnCapabilitiesImpl.createDefault().setType(queryType);
|
||||
return DimensionHandlerUtils.getHandlerFromCapabilities(column, capabilities, null)
|
||||
.getDimensionSchema(capabilities);
|
||||
}
|
||||
|
||||
if (type != null && (type.isPrimitive() || type.isPrimitiveArray())) {
|
||||
return new AutoTypeColumnSchema(column, type);
|
||||
if (queryType != null && (queryType.isPrimitive() || queryType.isPrimitiveArray())) {
|
||||
return new AutoTypeColumnSchema(column, queryType);
|
||||
}
|
||||
return new AutoTypeColumnSchema(column, null);
|
||||
} else {
|
||||
// if schema information is not available, create a string dimension
|
||||
if (type == null) {
|
||||
return new StringDimensionSchema(column);
|
||||
} else if (type.getType() == ValueType.STRING) {
|
||||
return new StringDimensionSchema(column);
|
||||
} else if (type.getType() == ValueType.LONG) {
|
||||
// dimensionType may not be identical to queryType, depending on arrayIngestMode.
|
||||
final ColumnType dimensionType = getDimensionType(queryType, arrayIngestMode);
|
||||
|
||||
if (dimensionType.getType() == ValueType.STRING) {
|
||||
return new StringDimensionSchema(
|
||||
column,
|
||||
queryType != null && queryType.isArray()
|
||||
? DimensionSchema.MultiValueHandling.ARRAY
|
||||
: DimensionSchema.MultiValueHandling.SORTED_ARRAY,
|
||||
null
|
||||
);
|
||||
} else if (dimensionType.getType() == ValueType.LONG) {
|
||||
return new LongDimensionSchema(column);
|
||||
} else if (type.getType() == ValueType.FLOAT) {
|
||||
} else if (dimensionType.getType() == ValueType.FLOAT) {
|
||||
return new FloatDimensionSchema(column);
|
||||
} else if (type.getType() == ValueType.DOUBLE) {
|
||||
} else if (dimensionType.getType() == ValueType.DOUBLE) {
|
||||
return new DoubleDimensionSchema(column);
|
||||
} else if (type.getType() == ValueType.ARRAY) {
|
||||
ValueType elementType = type.getElementType().getType();
|
||||
if (elementType == ValueType.STRING) {
|
||||
if (arrayIngestMode == ArrayIngestMode.NONE) {
|
||||
throw InvalidInput.exception(
|
||||
"String arrays can not be ingested when '%s' is set to '%s'. Set '%s' in query context "
|
||||
+ "to 'array' to ingest the string array as an array, or ingest it as an MVD by explicitly casting the "
|
||||
+ "array to an MVD with ARRAY_TO_MV function.",
|
||||
MultiStageQueryContext.CTX_ARRAY_INGEST_MODE,
|
||||
StringUtils.toLowerCase(arrayIngestMode.name()),
|
||||
MultiStageQueryContext.CTX_ARRAY_INGEST_MODE
|
||||
);
|
||||
} else if (arrayIngestMode == ArrayIngestMode.MVD) {
|
||||
return new StringDimensionSchema(column, DimensionSchema.MultiValueHandling.ARRAY, null);
|
||||
} else {
|
||||
// arrayIngestMode == ArrayIngestMode.ARRAY would be true
|
||||
return new AutoTypeColumnSchema(column, type);
|
||||
}
|
||||
} else if (elementType.isNumeric()) {
|
||||
// ValueType == LONG || ValueType == FLOAT || ValueType == DOUBLE
|
||||
if (arrayIngestMode == ArrayIngestMode.ARRAY) {
|
||||
return new AutoTypeColumnSchema(column, type);
|
||||
} else {
|
||||
throw InvalidInput.exception(
|
||||
"Numeric arrays can only be ingested when '%s' is set to 'array' in the MSQ query's context. "
|
||||
+ "Current value of the parameter [%s]",
|
||||
MultiStageQueryContext.CTX_ARRAY_INGEST_MODE,
|
||||
StringUtils.toLowerCase(arrayIngestMode.name())
|
||||
);
|
||||
}
|
||||
} else {
|
||||
throw new ISE("Cannot create dimension for type [%s]", type.toString());
|
||||
}
|
||||
} else if (dimensionType.getType() == ValueType.ARRAY) {
|
||||
return new AutoTypeColumnSchema(column, dimensionType);
|
||||
} else {
|
||||
final ColumnCapabilities capabilities = ColumnCapabilitiesImpl.createDefault().setType(type);
|
||||
final ColumnCapabilities capabilities = ColumnCapabilitiesImpl.createDefault().setType(dimensionType);
|
||||
return DimensionHandlerUtils.getHandlerFromCapabilities(column, capabilities, null)
|
||||
.getDimensionSchema(capabilities);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Based on a type from a query result, get the type of dimension we should write.
|
||||
*
|
||||
* @throws org.apache.druid.error.DruidException if there is some problem
|
||||
*/
|
||||
public static ColumnType getDimensionType(
|
||||
@Nullable final ColumnType queryType,
|
||||
final ArrayIngestMode arrayIngestMode
|
||||
)
|
||||
{
|
||||
if (queryType == null) {
|
||||
// if schema information is not available, create a string dimension
|
||||
return ColumnType.STRING;
|
||||
} else if (queryType.getType() == ValueType.ARRAY) {
|
||||
ValueType elementType = queryType.getElementType().getType();
|
||||
if (elementType == ValueType.STRING) {
|
||||
if (arrayIngestMode == ArrayIngestMode.NONE) {
|
||||
throw InvalidInput.exception(
|
||||
"String arrays can not be ingested when '%s' is set to '%s'. Set '%s' in query context "
|
||||
+ "to 'array' to ingest the string array as an array, or ingest it as an MVD by explicitly casting the "
|
||||
+ "array to an MVD with the ARRAY_TO_MV function.",
|
||||
MultiStageQueryContext.CTX_ARRAY_INGEST_MODE,
|
||||
StringUtils.toLowerCase(arrayIngestMode.name()),
|
||||
MultiStageQueryContext.CTX_ARRAY_INGEST_MODE
|
||||
);
|
||||
} else if (arrayIngestMode == ArrayIngestMode.MVD) {
|
||||
return ColumnType.STRING;
|
||||
} else {
|
||||
assert arrayIngestMode == ArrayIngestMode.ARRAY;
|
||||
return queryType;
|
||||
}
|
||||
} else if (elementType.isNumeric()) {
|
||||
// ValueType == LONG || ValueType == FLOAT || ValueType == DOUBLE
|
||||
if (arrayIngestMode == ArrayIngestMode.ARRAY) {
|
||||
return queryType;
|
||||
} else {
|
||||
throw InvalidInput.exception(
|
||||
"Numeric arrays can only be ingested when '%s' is set to 'array'. "
|
||||
+ "Current value of the parameter is[%s]",
|
||||
MultiStageQueryContext.CTX_ARRAY_INGEST_MODE,
|
||||
StringUtils.toLowerCase(arrayIngestMode.name())
|
||||
);
|
||||
}
|
||||
} else {
|
||||
throw new ISE("Cannot create dimension for type[%s]", queryType.toString());
|
||||
}
|
||||
} else {
|
||||
return queryType;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -43,7 +43,9 @@ import javax.annotation.Nullable;
|
|||
import java.io.IOException;
|
||||
import java.util.Arrays;
|
||||
import java.util.Collections;
|
||||
import java.util.HashSet;
|
||||
import java.util.List;
|
||||
import java.util.Set;
|
||||
import java.util.regex.Pattern;
|
||||
import java.util.stream.Collectors;
|
||||
|
||||
|
|
@ -152,6 +154,7 @@ public class MultiStageQueryContext
|
|||
public static final String CTX_ARRAY_INGEST_MODE = "arrayIngestMode";
|
||||
public static final ArrayIngestMode DEFAULT_ARRAY_INGEST_MODE = ArrayIngestMode.MVD;
|
||||
|
||||
public static final String CTX_SKIP_TYPE_VERIFICATION = "skipTypeVerification";
|
||||
|
||||
private static final Pattern LOOKS_LIKE_JSON_ARRAY = Pattern.compile("^\\s*\\[.*", Pattern.DOTALL);
|
||||
|
||||
|
|
@ -297,7 +300,7 @@ public class MultiStageQueryContext
|
|||
|
||||
public static List<String> getSortOrder(final QueryContext queryContext)
|
||||
{
|
||||
return MultiStageQueryContext.decodeSortOrder(queryContext.getString(CTX_SORT_ORDER));
|
||||
return decodeList(CTX_SORT_ORDER, queryContext.getString(CTX_SORT_ORDER));
|
||||
}
|
||||
|
||||
@Nullable
|
||||
|
|
@ -316,37 +319,39 @@ public class MultiStageQueryContext
|
|||
return queryContext.getEnum(CTX_ARRAY_INGEST_MODE, ArrayIngestMode.class, DEFAULT_ARRAY_INGEST_MODE);
|
||||
}
|
||||
|
||||
/**
|
||||
* Decodes {@link #CTX_SORT_ORDER} from either a JSON or CSV string.
|
||||
*/
|
||||
@Nullable
|
||||
@VisibleForTesting
|
||||
static List<String> decodeSortOrder(@Nullable final String sortOrderString)
|
||||
public static Set<String> getColumnsExcludedFromTypeVerification(final QueryContext queryContext)
|
||||
{
|
||||
if (sortOrderString == null) {
|
||||
return new HashSet<>(decodeList(CTX_SKIP_TYPE_VERIFICATION, queryContext.getString(CTX_SKIP_TYPE_VERIFICATION)));
|
||||
}
|
||||
|
||||
/**
|
||||
* Decodes a list from either a JSON or CSV string.
|
||||
*/
|
||||
@VisibleForTesting
|
||||
static List<String> decodeList(final String keyName, @Nullable final String listString)
|
||||
{
|
||||
if (listString == null) {
|
||||
return Collections.emptyList();
|
||||
} else if (LOOKS_LIKE_JSON_ARRAY.matcher(sortOrderString).matches()) {
|
||||
} else if (LOOKS_LIKE_JSON_ARRAY.matcher(listString).matches()) {
|
||||
try {
|
||||
// Not caching this ObjectMapper in a static, because we expect to use it infrequently (once per INSERT
|
||||
// query that uses this feature) and there is no need to keep it around longer than that.
|
||||
return new ObjectMapper().readValue(sortOrderString, new TypeReference<List<String>>()
|
||||
{
|
||||
});
|
||||
return new ObjectMapper().readValue(listString, new TypeReference<List<String>>() {});
|
||||
}
|
||||
catch (JsonProcessingException e) {
|
||||
throw QueryContexts.badValueException(CTX_SORT_ORDER, "CSV or JSON array", sortOrderString);
|
||||
throw QueryContexts.badValueException(keyName, "CSV or JSON array", listString);
|
||||
}
|
||||
} else {
|
||||
final RFC4180Parser csvParser = new RFC4180ParserBuilder().withSeparator(',').build();
|
||||
|
||||
try {
|
||||
return Arrays.stream(csvParser.parseLine(sortOrderString))
|
||||
return Arrays.stream(csvParser.parseLine(listString))
|
||||
.filter(s -> s != null && !s.isEmpty())
|
||||
.map(String::trim)
|
||||
.collect(Collectors.toList());
|
||||
}
|
||||
catch (IOException e) {
|
||||
throw QueryContexts.badValueException(CTX_SORT_ORDER, "CSV or JSON array", sortOrderString);
|
||||
throw QueryContexts.badValueException(keyName, "CSV or JSON array", listString);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -21,10 +21,12 @@ package org.apache.druid.msq.exec;
|
|||
|
||||
import com.google.common.collect.ImmutableList;
|
||||
import com.google.common.collect.ImmutableSet;
|
||||
import org.apache.druid.common.config.NullHandling;
|
||||
import org.apache.druid.data.input.impl.InlineInputSource;
|
||||
import org.apache.druid.data.input.impl.JsonInputFormat;
|
||||
import org.apache.druid.data.input.impl.LocalInputSource;
|
||||
import org.apache.druid.data.input.impl.systemfield.SystemFields;
|
||||
import org.apache.druid.error.DruidException;
|
||||
import org.apache.druid.java.util.common.ISE;
|
||||
import org.apache.druid.java.util.common.Intervals;
|
||||
import org.apache.druid.msq.indexing.MSQSpec;
|
||||
|
|
@ -111,14 +113,14 @@ public class MSQArraysTest extends MSQTestBase
|
|||
dataFileNameJsonString = queryFramework().queryJsonMapper().writeValueAsString(dataFile);
|
||||
|
||||
RowSignature dataFileSignature = RowSignature.builder()
|
||||
.add("timestamp", ColumnType.STRING)
|
||||
.add("arrayString", ColumnType.STRING_ARRAY)
|
||||
.add("arrayStringNulls", ColumnType.STRING_ARRAY)
|
||||
.add("arrayLong", ColumnType.LONG_ARRAY)
|
||||
.add("arrayLongNulls", ColumnType.LONG_ARRAY)
|
||||
.add("arrayDouble", ColumnType.DOUBLE_ARRAY)
|
||||
.add("arrayDoubleNulls", ColumnType.DOUBLE_ARRAY)
|
||||
.build();
|
||||
.add("timestamp", ColumnType.STRING)
|
||||
.add("arrayString", ColumnType.STRING_ARRAY)
|
||||
.add("arrayStringNulls", ColumnType.STRING_ARRAY)
|
||||
.add("arrayLong", ColumnType.LONG_ARRAY)
|
||||
.add("arrayLongNulls", ColumnType.LONG_ARRAY)
|
||||
.add("arrayDouble", ColumnType.DOUBLE_ARRAY)
|
||||
.add("arrayDoubleNulls", ColumnType.DOUBLE_ARRAY)
|
||||
.build();
|
||||
dataFileSignatureJsonString = queryFramework().queryJsonMapper().writeValueAsString(dataFileSignature);
|
||||
|
||||
dataFileExternalDataSource = new ExternalDataSource(
|
||||
|
|
@ -150,6 +152,171 @@ public class MSQArraysTest extends MSQTestBase
|
|||
.verifyExecutionError();
|
||||
}
|
||||
|
||||
/**
|
||||
* Tests the behaviour of INSERT query when arrayIngestMode is set to none (default) and the user tries to ingest
|
||||
* string arrays
|
||||
*/
|
||||
@Test
|
||||
public void testReplaceMvdWithStringArray()
|
||||
{
|
||||
final Map<String, Object> adjustedContext = new HashMap<>(context);
|
||||
adjustedContext.put(MultiStageQueryContext.CTX_ARRAY_INGEST_MODE, "array");
|
||||
|
||||
testIngestQuery()
|
||||
.setSql(
|
||||
"REPLACE INTO foo OVERWRITE ALL\n"
|
||||
+ "SELECT MV_TO_ARRAY(dim3) AS dim3 FROM foo\n"
|
||||
+ "PARTITIONED BY ALL TIME"
|
||||
)
|
||||
.setQueryContext(adjustedContext)
|
||||
.setExpectedExecutionErrorMatcher(CoreMatchers.allOf(
|
||||
CoreMatchers.instanceOf(DruidException.class),
|
||||
ThrowableMessageMatcher.hasMessage(CoreMatchers.startsWith(
|
||||
"Cannot write into field[dim3] using type[VARCHAR ARRAY] and arrayIngestMode[array], "
|
||||
+ "since the existing type is[VARCHAR]"))
|
||||
))
|
||||
.verifyExecutionError();
|
||||
}
|
||||
|
||||
/**
|
||||
* Tests the behaviour of INSERT query when arrayIngestMode is set to none (default) and the user tries to ingest
|
||||
* string arrays
|
||||
*/
|
||||
@Test
|
||||
public void testReplaceStringArrayWithMvdInArrayMode()
|
||||
{
|
||||
final Map<String, Object> adjustedContext = new HashMap<>(context);
|
||||
adjustedContext.put(MultiStageQueryContext.CTX_ARRAY_INGEST_MODE, "array");
|
||||
|
||||
testIngestQuery()
|
||||
.setSql(
|
||||
"REPLACE INTO arrays OVERWRITE ALL\n"
|
||||
+ "SELECT ARRAY_TO_MV(arrayString) AS arrayString FROM arrays\n"
|
||||
+ "PARTITIONED BY ALL TIME"
|
||||
)
|
||||
.setQueryContext(adjustedContext)
|
||||
.setExpectedExecutionErrorMatcher(CoreMatchers.allOf(
|
||||
CoreMatchers.instanceOf(DruidException.class),
|
||||
ThrowableMessageMatcher.hasMessage(CoreMatchers.startsWith(
|
||||
"Cannot write into field[arrayString] using type[VARCHAR] and arrayIngestMode[array], since the "
|
||||
+ "existing type is[VARCHAR ARRAY]. Try adjusting your query to make this column an ARRAY instead "
|
||||
+ "of VARCHAR."))
|
||||
))
|
||||
.verifyExecutionError();
|
||||
}
|
||||
|
||||
/**
|
||||
* Tests the behaviour of INSERT query when arrayIngestMode is set to none (default) and the user tries to ingest
|
||||
* string arrays
|
||||
*/
|
||||
@Test
|
||||
public void testReplaceStringArrayWithMvdInMvdMode()
|
||||
{
|
||||
final Map<String, Object> adjustedContext = new HashMap<>(context);
|
||||
adjustedContext.put(MultiStageQueryContext.CTX_ARRAY_INGEST_MODE, "mvd");
|
||||
|
||||
testIngestQuery()
|
||||
.setSql(
|
||||
"REPLACE INTO arrays OVERWRITE ALL\n"
|
||||
+ "SELECT ARRAY_TO_MV(arrayString) AS arrayString FROM arrays\n"
|
||||
+ "PARTITIONED BY ALL TIME"
|
||||
)
|
||||
.setQueryContext(adjustedContext)
|
||||
.setExpectedExecutionErrorMatcher(CoreMatchers.allOf(
|
||||
CoreMatchers.instanceOf(DruidException.class),
|
||||
ThrowableMessageMatcher.hasMessage(CoreMatchers.startsWith(
|
||||
"Cannot write into field[arrayString] using type[VARCHAR] and arrayIngestMode[mvd], since the "
|
||||
+ "existing type is[VARCHAR ARRAY]. Try setting arrayIngestMode to[array] and adjusting your query to "
|
||||
+ "make this column an ARRAY instead of VARCHAR."))
|
||||
))
|
||||
.verifyExecutionError();
|
||||
}
|
||||
|
||||
/**
|
||||
* Tests the behaviour of INSERT query when arrayIngestMode is set to none (default) and the user tries to ingest
|
||||
* string arrays
|
||||
*/
|
||||
@Test
|
||||
public void testReplaceMvdWithStringArraySkipValidation()
|
||||
{
|
||||
final Map<String, Object> adjustedContext = new HashMap<>(context);
|
||||
adjustedContext.put(MultiStageQueryContext.CTX_ARRAY_INGEST_MODE, "array");
|
||||
adjustedContext.put(MultiStageQueryContext.CTX_SKIP_TYPE_VERIFICATION, "dim3");
|
||||
|
||||
RowSignature rowSignature = RowSignature.builder()
|
||||
.add("__time", ColumnType.LONG)
|
||||
.add("dim3", ColumnType.STRING_ARRAY)
|
||||
.build();
|
||||
|
||||
testIngestQuery()
|
||||
.setSql(
|
||||
"REPLACE INTO foo OVERWRITE ALL\n"
|
||||
+ "SELECT MV_TO_ARRAY(dim3) AS dim3 FROM foo\n"
|
||||
+ "PARTITIONED BY ALL TIME"
|
||||
)
|
||||
.setQueryContext(adjustedContext)
|
||||
.setExpectedDataSource("foo")
|
||||
.setExpectedRowSignature(rowSignature)
|
||||
.setExpectedSegment(ImmutableSet.of(SegmentId.of("foo", Intervals.ETERNITY, "test", 0)))
|
||||
.setExpectedResultRows(
|
||||
NullHandling.sqlCompatible()
|
||||
? ImmutableList.of(
|
||||
new Object[]{0L, null},
|
||||
new Object[]{0L, null},
|
||||
new Object[]{0L, new Object[]{"a", "b"}},
|
||||
new Object[]{0L, new Object[]{""}},
|
||||
new Object[]{0L, new Object[]{"b", "c"}},
|
||||
new Object[]{0L, new Object[]{"d"}}
|
||||
)
|
||||
: ImmutableList.of(
|
||||
new Object[]{0L, null},
|
||||
new Object[]{0L, null},
|
||||
new Object[]{0L, null},
|
||||
new Object[]{0L, new Object[]{"a", "b"}},
|
||||
new Object[]{0L, new Object[]{"b", "c"}},
|
||||
new Object[]{0L, new Object[]{"d"}}
|
||||
)
|
||||
)
|
||||
.verifyResults();
|
||||
}
|
||||
|
||||
/**
|
||||
* Tests the behaviour of INSERT query when arrayIngestMode is set to none (default) and the user tries to ingest
|
||||
* string arrays
|
||||
*/
|
||||
@Test
|
||||
public void testReplaceMvdWithMvd()
|
||||
{
|
||||
final Map<String, Object> adjustedContext = new HashMap<>(context);
|
||||
adjustedContext.put(MultiStageQueryContext.CTX_ARRAY_INGEST_MODE, "array");
|
||||
|
||||
RowSignature rowSignature = RowSignature.builder()
|
||||
.add("__time", ColumnType.LONG)
|
||||
.add("dim3", ColumnType.STRING)
|
||||
.build();
|
||||
|
||||
testIngestQuery()
|
||||
.setSql(
|
||||
"REPLACE INTO foo OVERWRITE ALL\n"
|
||||
+ "SELECT dim3 FROM foo\n"
|
||||
+ "PARTITIONED BY ALL TIME"
|
||||
)
|
||||
.setQueryContext(adjustedContext)
|
||||
.setExpectedDataSource("foo")
|
||||
.setExpectedRowSignature(rowSignature)
|
||||
.setExpectedSegment(ImmutableSet.of(SegmentId.of("foo", Intervals.ETERNITY, "test", 0)))
|
||||
.setExpectedResultRows(
|
||||
ImmutableList.of(
|
||||
new Object[]{0L, null},
|
||||
new Object[]{0L, null},
|
||||
new Object[]{0L, NullHandling.sqlCompatible() ? "" : null},
|
||||
new Object[]{0L, ImmutableList.of("a", "b")},
|
||||
new Object[]{0L, ImmutableList.of("b", "c")},
|
||||
new Object[]{0L, "d"}
|
||||
)
|
||||
)
|
||||
.verifyResults();
|
||||
}
|
||||
|
||||
/**
|
||||
* Tests the behaviour of INSERT query when arrayIngestMode is set to mvd (default) and the only array type to be
|
||||
|
|
@ -475,7 +642,7 @@ public class MSQArraysTest extends MSQTestBase
|
|||
null,
|
||||
Arrays.asList(3.3d, 4.4d, 5.5d),
|
||||
Arrays.asList(999.0d, null, 5.5d),
|
||||
},
|
||||
},
|
||||
new Object[]{
|
||||
1672531200000L,
|
||||
Arrays.asList("b", "c"),
|
||||
|
|
@ -583,7 +750,7 @@ public class MSQArraysTest extends MSQTestBase
|
|||
Arrays.asList(2L, 3L),
|
||||
null,
|
||||
Arrays.asList(null, 1.1d),
|
||||
}
|
||||
}
|
||||
);
|
||||
|
||||
RowSignature rowSignatureWithoutTimeColumn =
|
||||
|
|
|
|||
|
|
@ -1215,7 +1215,7 @@ public class MSQInsertTest extends MSQTestBase
|
|||
DruidException.Persona.USER,
|
||||
DruidException.Category.INVALID_INPUT,
|
||||
"invalidInput"
|
||||
).expectMessageIs("Field [__time] was the wrong type [VARCHAR], expected TIMESTAMP")
|
||||
).expectMessageIs("Field[__time] was the wrong type[VARCHAR], expected TIMESTAMP")
|
||||
)
|
||||
.verifyPlanningErrors();
|
||||
}
|
||||
|
|
|
|||
|
|
@ -76,7 +76,6 @@ import org.apache.druid.segment.writeout.OffHeapMemorySegmentWriteOutMediumFacto
|
|||
import org.apache.druid.server.SegmentManager;
|
||||
import org.apache.druid.server.coordination.DataSegmentAnnouncer;
|
||||
import org.apache.druid.server.coordination.NoopDataSegmentAnnouncer;
|
||||
import org.apache.druid.sql.calcite.CalciteArraysQueryTest;
|
||||
import org.apache.druid.sql.calcite.util.CalciteTests;
|
||||
import org.apache.druid.sql.calcite.util.TestDataBuilder;
|
||||
import org.apache.druid.timeline.DataSegment;
|
||||
|
|
@ -92,6 +91,7 @@ import java.util.List;
|
|||
import java.util.Set;
|
||||
import java.util.function.Supplier;
|
||||
|
||||
import static org.apache.druid.sql.calcite.util.CalciteTests.ARRAYS_DATASOURCE;
|
||||
import static org.apache.druid.sql.calcite.util.CalciteTests.DATASOURCE1;
|
||||
import static org.apache.druid.sql.calcite.util.CalciteTests.DATASOURCE2;
|
||||
import static org.apache.druid.sql.calcite.util.CalciteTests.DATASOURCE3;
|
||||
|
|
@ -282,7 +282,7 @@ public class CalciteMSQTestsHelper
|
|||
.rows(ROWS_LOTS_OF_COLUMNS)
|
||||
.buildMMappedIndex();
|
||||
break;
|
||||
case CalciteArraysQueryTest.DATA_SOURCE_ARRAYS:
|
||||
case ARRAYS_DATASOURCE:
|
||||
index = IndexBuilder.create()
|
||||
.tmpDir(temporaryFolder.newFolder())
|
||||
.segmentWriteOutMediumFactory(OffHeapMemorySegmentWriteOutMediumFactory.instance())
|
||||
|
|
|
|||
|
|
@ -179,19 +179,19 @@ public class DimensionSchemaUtilsTest
|
|||
DruidException.class,
|
||||
() -> DimensionSchemaUtils.createDimensionSchema("x", ColumnType.LONG_ARRAY, false, ArrayIngestMode.MVD)
|
||||
);
|
||||
Assert.assertEquals("Numeric arrays can only be ingested when 'arrayIngestMode' is set to 'array' in the MSQ query's context. Current value of the parameter [mvd]", t.getMessage());
|
||||
Assert.assertEquals("Numeric arrays can only be ingested when 'arrayIngestMode' is set to 'array'. Current value of the parameter is[mvd]", t.getMessage());
|
||||
|
||||
t = Assert.assertThrows(
|
||||
DruidException.class,
|
||||
() -> DimensionSchemaUtils.createDimensionSchema("x", ColumnType.DOUBLE_ARRAY, false, ArrayIngestMode.MVD)
|
||||
);
|
||||
Assert.assertEquals("Numeric arrays can only be ingested when 'arrayIngestMode' is set to 'array' in the MSQ query's context. Current value of the parameter [mvd]", t.getMessage());
|
||||
Assert.assertEquals("Numeric arrays can only be ingested when 'arrayIngestMode' is set to 'array'. Current value of the parameter is[mvd]", t.getMessage());
|
||||
|
||||
t = Assert.assertThrows(
|
||||
DruidException.class,
|
||||
() -> DimensionSchemaUtils.createDimensionSchema("x", ColumnType.FLOAT_ARRAY, false, ArrayIngestMode.MVD)
|
||||
);
|
||||
Assert.assertEquals("Numeric arrays can only be ingested when 'arrayIngestMode' is set to 'array' in the MSQ query's context. Current value of the parameter [mvd]", t.getMessage());
|
||||
Assert.assertEquals("Numeric arrays can only be ingested when 'arrayIngestMode' is set to 'array'. Current value of the parameter is[mvd]", t.getMessage());
|
||||
}
|
||||
|
||||
@Test
|
||||
|
|
|
|||
|
|
@ -314,7 +314,7 @@ public class MultiStageQueryContextTest
|
|||
|
||||
private static List<String> decodeSortOrder(@Nullable final String input)
|
||||
{
|
||||
return MultiStageQueryContext.decodeSortOrder(input);
|
||||
return MultiStageQueryContext.decodeList(MultiStageQueryContext.CTX_SORT_ORDER, input);
|
||||
}
|
||||
|
||||
private static IndexSpec decodeIndexSpec(@Nullable final Object inputSpecObject)
|
||||
|
|
|
|||
|
|
@ -524,6 +524,12 @@ public class DruidAvaticaHandlerTest extends CalciteTestBase
|
|||
final DatabaseMetaData metaData = client.getMetaData();
|
||||
Assert.assertEquals(
|
||||
ImmutableList.of(
|
||||
row(
|
||||
Pair.of("TABLE_CAT", "druid"),
|
||||
Pair.of("TABLE_NAME", CalciteTests.ARRAYS_DATASOURCE),
|
||||
Pair.of("TABLE_SCHEM", "druid"),
|
||||
Pair.of("TABLE_TYPE", "TABLE")
|
||||
),
|
||||
row(
|
||||
Pair.of("TABLE_CAT", "druid"),
|
||||
Pair.of("TABLE_NAME", CalciteTests.BROADCAST_DATASOURCE),
|
||||
|
|
@ -605,6 +611,12 @@ public class DruidAvaticaHandlerTest extends CalciteTestBase
|
|||
final DatabaseMetaData metaData = superuserClient.getMetaData();
|
||||
Assert.assertEquals(
|
||||
ImmutableList.of(
|
||||
row(
|
||||
Pair.of("TABLE_CAT", "druid"),
|
||||
Pair.of("TABLE_NAME", CalciteTests.ARRAYS_DATASOURCE),
|
||||
Pair.of("TABLE_SCHEM", "druid"),
|
||||
Pair.of("TABLE_TYPE", "TABLE")
|
||||
),
|
||||
row(
|
||||
Pair.of("TABLE_CAT", "druid"),
|
||||
Pair.of("TABLE_NAME", CalciteTests.BROADCAST_DATASOURCE),
|
||||
|
|
|
|||
|
|
@ -22,9 +22,7 @@ package org.apache.druid.sql.calcite;
|
|||
import com.google.common.collect.ImmutableList;
|
||||
import com.google.common.collect.ImmutableMap;
|
||||
import com.google.common.collect.ImmutableSet;
|
||||
import com.google.inject.Injector;
|
||||
import org.apache.druid.common.config.NullHandling;
|
||||
import org.apache.druid.data.input.ResourceInputSource;
|
||||
import org.apache.druid.guice.DruidInjectorBuilder;
|
||||
import org.apache.druid.guice.NestedDataModule;
|
||||
import org.apache.druid.java.util.common.HumanReadableBytes;
|
||||
|
|
@ -34,17 +32,13 @@ import org.apache.druid.java.util.common.granularity.Granularities;
|
|||
import org.apache.druid.math.expr.ExprEval;
|
||||
import org.apache.druid.math.expr.ExprMacroTable;
|
||||
import org.apache.druid.math.expr.ExpressionType;
|
||||
import org.apache.druid.query.DataSource;
|
||||
import org.apache.druid.query.Druids;
|
||||
import org.apache.druid.query.FilteredDataSource;
|
||||
import org.apache.druid.query.FrameBasedInlineDataSource;
|
||||
import org.apache.druid.query.InlineDataSource;
|
||||
import org.apache.druid.query.LookupDataSource;
|
||||
import org.apache.druid.query.NestedDataTestUtils;
|
||||
import org.apache.druid.query.Query;
|
||||
import org.apache.druid.query.QueryContexts;
|
||||
import org.apache.druid.query.QueryDataSource;
|
||||
import org.apache.druid.query.QueryRunnerFactoryConglomerate;
|
||||
import org.apache.druid.query.TableDataSource;
|
||||
import org.apache.druid.query.UnnestDataSource;
|
||||
import org.apache.druid.query.aggregation.CountAggregatorFactory;
|
||||
|
|
@ -65,37 +59,20 @@ import org.apache.druid.query.groupby.having.DimFilterHavingSpec;
|
|||
import org.apache.druid.query.groupby.orderby.DefaultLimitSpec;
|
||||
import org.apache.druid.query.groupby.orderby.NoopLimitSpec;
|
||||
import org.apache.druid.query.groupby.orderby.OrderByColumnSpec;
|
||||
import org.apache.druid.query.lookup.LookupExtractorFactoryContainerProvider;
|
||||
import org.apache.druid.query.ordering.StringComparators;
|
||||
import org.apache.druid.query.scan.ScanQuery;
|
||||
import org.apache.druid.query.spec.MultipleIntervalSegmentSpec;
|
||||
import org.apache.druid.query.topn.DimensionTopNMetricSpec;
|
||||
import org.apache.druid.query.topn.TopNQueryBuilder;
|
||||
import org.apache.druid.segment.FrameBasedInlineSegmentWrangler;
|
||||
import org.apache.druid.segment.IndexBuilder;
|
||||
import org.apache.druid.segment.InlineSegmentWrangler;
|
||||
import org.apache.druid.segment.LookupSegmentWrangler;
|
||||
import org.apache.druid.segment.MapSegmentWrangler;
|
||||
import org.apache.druid.segment.QueryableIndex;
|
||||
import org.apache.druid.segment.SegmentWrangler;
|
||||
import org.apache.druid.segment.column.ColumnType;
|
||||
import org.apache.druid.segment.column.RowSignature;
|
||||
import org.apache.druid.segment.incremental.IncrementalIndexSchema;
|
||||
import org.apache.druid.segment.join.JoinType;
|
||||
import org.apache.druid.segment.join.JoinableFactoryWrapper;
|
||||
import org.apache.druid.segment.virtual.ExpressionVirtualColumn;
|
||||
import org.apache.druid.segment.writeout.OffHeapMemorySegmentWriteOutMediumFactory;
|
||||
import org.apache.druid.server.QueryStackTests;
|
||||
import org.apache.druid.server.SpecificSegmentsQuerySegmentWalker;
|
||||
import org.apache.druid.sql.calcite.filtration.Filtration;
|
||||
import org.apache.druid.sql.calcite.util.CalciteTests;
|
||||
import org.apache.druid.sql.calcite.util.TestDataBuilder;
|
||||
import org.apache.druid.timeline.DataSegment;
|
||||
import org.apache.druid.timeline.partition.LinearShardSpec;
|
||||
import org.junit.Assert;
|
||||
import org.junit.Test;
|
||||
|
||||
import java.io.IOException;
|
||||
import java.util.Arrays;
|
||||
import java.util.Collections;
|
||||
import java.util.List;
|
||||
|
|
@ -112,9 +89,6 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
.put(QueryContexts.CTX_SQL_STRINGIFY_ARRAYS, false)
|
||||
.build();
|
||||
|
||||
|
||||
public static final String DATA_SOURCE_ARRAYS = "arrays";
|
||||
|
||||
public static void assertResultsDeepEquals(String sql, List<Object[]> expected, List<Object[]> results)
|
||||
{
|
||||
for (int row = 0; row < results.size(); row++) {
|
||||
|
|
@ -146,121 +120,6 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
builder.addModule(new NestedDataModule());
|
||||
}
|
||||
|
||||
@SuppressWarnings("resource")
|
||||
@Override
|
||||
public SpecificSegmentsQuerySegmentWalker createQuerySegmentWalker(
|
||||
final QueryRunnerFactoryConglomerate conglomerate,
|
||||
final JoinableFactoryWrapper joinableFactory,
|
||||
final Injector injector
|
||||
) throws IOException
|
||||
{
|
||||
NestedDataModule.registerHandlersAndSerde();
|
||||
|
||||
final QueryableIndex foo = IndexBuilder
|
||||
.create()
|
||||
.tmpDir(temporaryFolder.newFolder())
|
||||
.segmentWriteOutMediumFactory(OffHeapMemorySegmentWriteOutMediumFactory.instance())
|
||||
.schema(TestDataBuilder.INDEX_SCHEMA)
|
||||
.rows(TestDataBuilder.ROWS1)
|
||||
.buildMMappedIndex();
|
||||
|
||||
final QueryableIndex numfoo = IndexBuilder
|
||||
.create()
|
||||
.tmpDir(temporaryFolder.newFolder())
|
||||
.segmentWriteOutMediumFactory(OffHeapMemorySegmentWriteOutMediumFactory.instance())
|
||||
.schema(TestDataBuilder.INDEX_SCHEMA_NUMERIC_DIMS)
|
||||
.rows(TestDataBuilder.ROWS1_WITH_NUMERIC_DIMS)
|
||||
.buildMMappedIndex();
|
||||
|
||||
final QueryableIndex indexLotsOfColumns = IndexBuilder
|
||||
.create()
|
||||
.tmpDir(temporaryFolder.newFolder())
|
||||
.segmentWriteOutMediumFactory(OffHeapMemorySegmentWriteOutMediumFactory.instance())
|
||||
.schema(TestDataBuilder.INDEX_SCHEMA_LOTS_O_COLUMNS)
|
||||
.rows(TestDataBuilder.ROWS_LOTS_OF_COLUMNS)
|
||||
.buildMMappedIndex();
|
||||
|
||||
final QueryableIndex indexArrays =
|
||||
IndexBuilder.create()
|
||||
.tmpDir(temporaryFolder.newFolder())
|
||||
.segmentWriteOutMediumFactory(OffHeapMemorySegmentWriteOutMediumFactory.instance())
|
||||
.schema(
|
||||
new IncrementalIndexSchema.Builder()
|
||||
.withTimestampSpec(NestedDataTestUtils.AUTO_SCHEMA.getTimestampSpec())
|
||||
.withDimensionsSpec(NestedDataTestUtils.AUTO_SCHEMA.getDimensionsSpec())
|
||||
.withMetrics(
|
||||
new CountAggregatorFactory("cnt")
|
||||
)
|
||||
.withRollup(false)
|
||||
.build()
|
||||
)
|
||||
.inputSource(
|
||||
ResourceInputSource.of(
|
||||
NestedDataTestUtils.class.getClassLoader(),
|
||||
NestedDataTestUtils.ARRAY_TYPES_DATA_FILE
|
||||
)
|
||||
)
|
||||
.inputFormat(TestDataBuilder.DEFAULT_JSON_INPUT_FORMAT)
|
||||
.inputTmpDir(temporaryFolder.newFolder())
|
||||
.buildMMappedIndex();
|
||||
|
||||
SpecificSegmentsQuerySegmentWalker walker = SpecificSegmentsQuerySegmentWalker.createWalker(
|
||||
injector,
|
||||
conglomerate,
|
||||
new MapSegmentWrangler(
|
||||
ImmutableMap.<Class<? extends DataSource>, SegmentWrangler>builder()
|
||||
.put(InlineDataSource.class, new InlineSegmentWrangler())
|
||||
.put(FrameBasedInlineDataSource.class, new FrameBasedInlineSegmentWrangler())
|
||||
.put(
|
||||
LookupDataSource.class,
|
||||
new LookupSegmentWrangler(injector.getInstance(LookupExtractorFactoryContainerProvider.class))
|
||||
)
|
||||
.build()
|
||||
),
|
||||
joinableFactory,
|
||||
QueryStackTests.DEFAULT_NOOP_SCHEDULER
|
||||
);
|
||||
walker.add(
|
||||
DataSegment.builder()
|
||||
.dataSource(CalciteTests.DATASOURCE1)
|
||||
.interval(foo.getDataInterval())
|
||||
.version("1")
|
||||
.shardSpec(new LinearShardSpec(0))
|
||||
.size(0)
|
||||
.build(),
|
||||
foo
|
||||
).add(
|
||||
DataSegment.builder()
|
||||
.dataSource(CalciteTests.DATASOURCE3)
|
||||
.interval(numfoo.getDataInterval())
|
||||
.version("1")
|
||||
.shardSpec(new LinearShardSpec(0))
|
||||
.size(0)
|
||||
.build(),
|
||||
numfoo
|
||||
).add(
|
||||
DataSegment.builder()
|
||||
.dataSource(CalciteTests.DATASOURCE5)
|
||||
.interval(indexLotsOfColumns.getDataInterval())
|
||||
.version("1")
|
||||
.shardSpec(new LinearShardSpec(0))
|
||||
.size(0)
|
||||
.build(),
|
||||
indexLotsOfColumns
|
||||
).add(
|
||||
DataSegment.builder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.version("1")
|
||||
.interval(indexArrays.getDataInterval())
|
||||
.shardSpec(new LinearShardSpec(1))
|
||||
.size(0)
|
||||
.build(),
|
||||
indexArrays
|
||||
);
|
||||
|
||||
return walker;
|
||||
}
|
||||
|
||||
// test some query stuffs, sort of limited since no native array column types so either need to use constructor or
|
||||
// array aggregator
|
||||
@Test
|
||||
|
|
@ -323,7 +182,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
QUERY_CONTEXT_NO_STRINGIFY_ARRAY,
|
||||
ImmutableList.of(
|
||||
GroupByQuery.builder()
|
||||
.setDataSource(DATA_SOURCE_ARRAYS)
|
||||
.setDataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.setInterval(querySegmentSpec(Filtration.eternity()))
|
||||
.setVirtualColumns(expressionVirtualColumn(
|
||||
"v0",
|
||||
|
|
@ -648,7 +507,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
+ " FROM druid.arrays",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.virtualColumns(
|
||||
// these report as strings even though they are not, someday this will not be so
|
||||
|
|
@ -864,7 +723,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayStringNulls FROM druid.arrays WHERE ARRAY_OVERLAP(arrayStringNulls, ARRAY['a','b']) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(
|
||||
or(
|
||||
|
|
@ -895,7 +754,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayLongNulls FROM druid.arrays WHERE ARRAY_OVERLAP(arrayLongNulls, ARRAY[1, 2]) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(
|
||||
or(
|
||||
|
|
@ -926,7 +785,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayDoubleNulls FROM druid.arrays WHERE ARRAY_OVERLAP(arrayDoubleNulls, ARRAY[1.1, 2.2]) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(
|
||||
or(
|
||||
|
|
@ -1004,7 +863,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayStringNulls, arrayString FROM druid.arrays WHERE ARRAY_OVERLAP(arrayStringNulls, arrayString) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(expressionFilter("array_overlap(\"arrayStringNulls\",\"arrayString\")"))
|
||||
.columns("arrayString", "arrayStringNulls")
|
||||
|
|
@ -1030,7 +889,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayLongNulls, arrayLong FROM druid.arrays WHERE ARRAY_OVERLAP(arrayLongNulls, arrayLong) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(expressionFilter("array_overlap(\"arrayLongNulls\",\"arrayLong\")"))
|
||||
.columns("arrayLong", "arrayLongNulls")
|
||||
|
|
@ -1056,7 +915,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayDoubleNulls, arrayDouble FROM druid.arrays WHERE ARRAY_OVERLAP(arrayDoubleNulls, arrayDouble) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(expressionFilter("array_overlap(\"arrayDoubleNulls\",\"arrayDouble\")"))
|
||||
.columns("arrayDouble", "arrayDoubleNulls")
|
||||
|
|
@ -1108,7 +967,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayStringNulls FROM druid.arrays WHERE ARRAY_CONTAINS(arrayStringNulls, ARRAY['a','b']) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(
|
||||
and(
|
||||
|
|
@ -1138,7 +997,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayLongNulls FROM druid.arrays WHERE ARRAY_CONTAINS(arrayLongNulls, ARRAY[1, null]) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(
|
||||
and(
|
||||
|
|
@ -1166,7 +1025,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayDoubleNulls FROM druid.arrays WHERE ARRAY_CONTAINS(arrayDoubleNulls, ARRAY[1.1, null]) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(
|
||||
and(
|
||||
|
|
@ -1276,7 +1135,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayStringNulls, arrayString FROM druid.arrays WHERE ARRAY_CONTAINS(arrayStringNulls, arrayString) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(
|
||||
expressionFilter("array_contains(\"arrayStringNulls\",\"arrayString\")")
|
||||
|
|
@ -1300,7 +1159,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayLong, arrayLongNulls FROM druid.arrays WHERE ARRAY_CONTAINS(arrayLong, arrayLongNulls) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(
|
||||
expressionFilter("array_contains(\"arrayLong\",\"arrayLongNulls\")")
|
||||
|
|
@ -1327,7 +1186,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayDoubleNulls, arrayDouble FROM druid.arrays WHERE ARRAY_CONTAINS(arrayDoubleNulls, arrayDouble) LIMIT 5",
|
||||
ImmutableList.of(
|
||||
newScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.filters(
|
||||
expressionFilter("array_contains(\"arrayDoubleNulls\",\"arrayDouble\")")
|
||||
|
|
@ -1378,7 +1237,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
QUERY_CONTEXT_NO_STRINGIFY_ARRAY,
|
||||
ImmutableList.of(
|
||||
new Druids.ScanQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.virtualColumns(
|
||||
expressionVirtualColumn("v0", "array_slice(\"arrayString\",1)", ColumnType.STRING_ARRAY),
|
||||
|
|
@ -1463,7 +1322,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT arrayStringNulls, ARRAY_LENGTH(arrayStringNulls), SUM(cnt) FROM druid.arrays GROUP BY 1, 2 ORDER BY 2 DESC",
|
||||
ImmutableList.of(
|
||||
GroupByQuery.builder()
|
||||
.setDataSource(DATA_SOURCE_ARRAYS)
|
||||
.setDataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.setInterval(querySegmentSpec(Filtration.eternity()))
|
||||
.setGranularity(Granularities.ALL)
|
||||
.setVirtualColumns(expressionVirtualColumn("v0", "array_length(\"arrayStringNulls\")", ColumnType.LONG))
|
||||
|
|
@ -1843,7 +1702,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
QUERY_CONTEXT_NO_STRINGIFY_ARRAY,
|
||||
ImmutableList.of(
|
||||
GroupByQuery.builder()
|
||||
.setDataSource(DATA_SOURCE_ARRAYS)
|
||||
.setDataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.setInterval(querySegmentSpec(Filtration.eternity()))
|
||||
.setGranularity(Granularities.ALL)
|
||||
.setDimensions(
|
||||
|
|
@ -1940,7 +1799,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
QUERY_CONTEXT_NO_STRINGIFY_ARRAY,
|
||||
ImmutableList.of(
|
||||
GroupByQuery.builder()
|
||||
.setDataSource(DATA_SOURCE_ARRAYS)
|
||||
.setDataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.setInterval(querySegmentSpec(Filtration.eternity()))
|
||||
.setGranularity(Granularities.ALL)
|
||||
.setDimensions(
|
||||
|
|
@ -2984,7 +2843,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT ARRAY_AGG(arrayLongNulls), ARRAY_AGG(DISTINCT arrayDouble), ARRAY_AGG(DISTINCT arrayStringNulls) FILTER(WHERE arrayLong = ARRAY[2,3]) FROM arrays WHERE arrayDoubleNulls is not null",
|
||||
ImmutableList.of(
|
||||
Druids.newTimeseriesQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.granularity(Granularities.ALL)
|
||||
.filters(notNull("arrayDoubleNulls"))
|
||||
|
|
@ -3068,7 +2927,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
"SELECT ARRAY_CONCAT_AGG(arrayLongNulls), ARRAY_CONCAT_AGG(DISTINCT arrayDouble), ARRAY_CONCAT_AGG(DISTINCT arrayStringNulls) FILTER(WHERE arrayLong = ARRAY[2,3]) FROM arrays WHERE arrayDoubleNulls is not null",
|
||||
ImmutableList.of(
|
||||
Druids.newTimeseriesQueryBuilder()
|
||||
.dataSource(DATA_SOURCE_ARRAYS)
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.intervals(querySegmentSpec(Filtration.eternity()))
|
||||
.granularity(Granularities.ALL)
|
||||
.filters(notNull("arrayDoubleNulls"))
|
||||
|
|
@ -3926,7 +3785,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
ImmutableList.of(
|
||||
Druids.newScanQueryBuilder()
|
||||
.dataSource(UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayString\"", ColumnType.STRING_ARRAY),
|
||||
null
|
||||
))
|
||||
|
|
@ -3974,7 +3833,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
ImmutableList.of(
|
||||
Druids.newScanQueryBuilder()
|
||||
.dataSource(UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayStringNulls\"", ColumnType.STRING_ARRAY),
|
||||
null
|
||||
))
|
||||
|
|
@ -4021,7 +3880,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
ImmutableList.of(
|
||||
Druids.newScanQueryBuilder()
|
||||
.dataSource(UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayLong\"", ColumnType.LONG_ARRAY),
|
||||
null
|
||||
))
|
||||
|
|
@ -4075,7 +3934,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
ImmutableList.of(
|
||||
Druids.newScanQueryBuilder()
|
||||
.dataSource(UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayLongNulls\"", ColumnType.LONG_ARRAY),
|
||||
null
|
||||
))
|
||||
|
|
@ -4125,7 +3984,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
ImmutableList.of(
|
||||
Druids.newScanQueryBuilder()
|
||||
.dataSource(UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayDouble\"", ColumnType.DOUBLE_ARRAY),
|
||||
null
|
||||
))
|
||||
|
|
@ -4179,7 +4038,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
ImmutableList.of(
|
||||
Druids.newScanQueryBuilder()
|
||||
.dataSource(UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayDoubleNulls\"", ColumnType.DOUBLE_ARRAY),
|
||||
null
|
||||
))
|
||||
|
|
@ -4315,7 +4174,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
.dataSource(
|
||||
UnnestDataSource.create(
|
||||
UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn(
|
||||
"j0.unnest",
|
||||
"\"arrayStringNulls\"",
|
||||
|
|
@ -4632,7 +4491,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
UnnestDataSource.create(
|
||||
FilteredDataSource.create(
|
||||
UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn(
|
||||
"j0.unnest",
|
||||
"\"arrayLongNulls\"",
|
||||
|
|
@ -4782,7 +4641,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
UnnestDataSource.create(
|
||||
FilteredDataSource.create(
|
||||
UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn(
|
||||
"j0.unnest",
|
||||
"\"arrayLongNulls\"",
|
||||
|
|
@ -4893,7 +4752,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
ImmutableList.of(
|
||||
GroupByQuery.builder()
|
||||
.setDataSource(UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayStringNulls\"", ColumnType.STRING_ARRAY),
|
||||
null
|
||||
))
|
||||
|
|
@ -6497,7 +6356,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
ImmutableList.of(
|
||||
Druids.newTimeseriesQueryBuilder()
|
||||
.dataSource(UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayDoubleNulls\"", ColumnType.DOUBLE_ARRAY),
|
||||
null
|
||||
))
|
||||
|
|
@ -6584,7 +6443,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
ImmutableList.of(
|
||||
GroupByQuery.builder()
|
||||
.setDataSource(UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayLongNulls\"", ColumnType.LONG_ARRAY),
|
||||
NullHandling.sqlCompatible()
|
||||
? or(
|
||||
|
|
@ -6621,7 +6480,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
ImmutableList.of(
|
||||
GroupByQuery.builder()
|
||||
.setDataSource(UnnestDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayLongNulls\"", ColumnType.LONG_ARRAY),
|
||||
NullHandling.sqlCompatible()
|
||||
? or(
|
||||
|
|
@ -6739,7 +6598,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
Druids.newScanQueryBuilder()
|
||||
.dataSource(UnnestDataSource.create(
|
||||
FilteredDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
range("__time", ColumnType.LONG, 1672617600000L, 1672704600000L, false, false)
|
||||
),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayStringNulls\"", ColumnType.STRING_ARRAY),
|
||||
|
|
@ -6998,7 +6857,7 @@ public class CalciteArraysQueryTest extends BaseCalciteQueryTest
|
|||
.dataSource(
|
||||
UnnestDataSource.create(
|
||||
FilteredDataSource.create(
|
||||
new TableDataSource(DATA_SOURCE_ARRAYS),
|
||||
new TableDataSource(CalciteTests.ARRAYS_DATASOURCE),
|
||||
range("__time", ColumnType.LONG, 1672617600000L, 1672704600000L, false, false)
|
||||
),
|
||||
expressionVirtualColumn("j0.unnest", "\"arrayLongNulls\"", ColumnType.LONG_ARRAY),
|
||||
|
|
|
|||
|
|
@ -177,6 +177,7 @@ public class CalciteQueryTest extends BaseCalciteQueryTest
|
|||
+ "WHERE TABLE_TYPE IN ('SYSTEM_TABLE', 'TABLE', 'VIEW')",
|
||||
ImmutableList.of(),
|
||||
ImmutableList.<Object[]>builder()
|
||||
.add(new Object[]{"druid", CalciteTests.ARRAYS_DATASOURCE, "TABLE", "NO", "NO"})
|
||||
.add(new Object[]{"druid", CalciteTests.BROADCAST_DATASOURCE, "TABLE", "YES", "YES"})
|
||||
.add(new Object[]{"druid", CalciteTests.DATASOURCE1, "TABLE", "NO", "NO"})
|
||||
.add(new Object[]{"druid", CalciteTests.DATASOURCE2, "TABLE", "NO", "NO"})
|
||||
|
|
@ -217,6 +218,7 @@ public class CalciteQueryTest extends BaseCalciteQueryTest
|
|||
CalciteTests.SUPER_USER_AUTH_RESULT,
|
||||
ImmutableList.of(),
|
||||
ImmutableList.<Object[]>builder()
|
||||
.add(new Object[]{"druid", CalciteTests.ARRAYS_DATASOURCE, "TABLE", "NO", "NO"})
|
||||
.add(new Object[]{"druid", CalciteTests.BROADCAST_DATASOURCE, "TABLE", "YES", "YES"})
|
||||
.add(new Object[]{"druid", CalciteTests.DATASOURCE1, "TABLE", "NO", "NO"})
|
||||
.add(new Object[]{"druid", CalciteTests.DATASOURCE2, "TABLE", "NO", "NO"})
|
||||
|
|
|
|||
|
|
@ -114,6 +114,7 @@ public class CalciteTests
|
|||
public static final String DATASOURCE3 = "numfoo";
|
||||
public static final String DATASOURCE4 = "foo4";
|
||||
public static final String DATASOURCE5 = "lotsocolumns";
|
||||
public static final String ARRAYS_DATASOURCE = "arrays";
|
||||
public static final String BROADCAST_DATASOURCE = "broadcast";
|
||||
public static final String FORBIDDEN_DATASOURCE = "forbiddenDatasource";
|
||||
public static final String FORBIDDEN_DESTINATION = "forbiddenDestination";
|
||||
|
|
|
|||
|
|
@ -42,6 +42,7 @@ import org.apache.druid.java.util.common.parsers.JSONPathSpec;
|
|||
import org.apache.druid.query.DataSource;
|
||||
import org.apache.druid.query.GlobalTableDataSource;
|
||||
import org.apache.druid.query.InlineDataSource;
|
||||
import org.apache.druid.query.NestedDataTestUtils;
|
||||
import org.apache.druid.query.QueryRunnerFactoryConglomerate;
|
||||
import org.apache.druid.query.aggregation.CountAggregatorFactory;
|
||||
import org.apache.druid.query.aggregation.DoubleSumAggregatorFactory;
|
||||
|
|
@ -832,6 +833,30 @@ public class TestDataBuilder
|
|||
.rows(USER_VISIT_ROWS)
|
||||
.buildMMappedIndex();
|
||||
|
||||
final QueryableIndex arraysIndex = IndexBuilder
|
||||
.create()
|
||||
.tmpDir(new File(tmpDir, "9"))
|
||||
.segmentWriteOutMediumFactory(OffHeapMemorySegmentWriteOutMediumFactory.instance())
|
||||
.schema(
|
||||
new IncrementalIndexSchema.Builder()
|
||||
.withTimestampSpec(NestedDataTestUtils.AUTO_SCHEMA.getTimestampSpec())
|
||||
.withDimensionsSpec(NestedDataTestUtils.AUTO_SCHEMA.getDimensionsSpec())
|
||||
.withMetrics(
|
||||
new CountAggregatorFactory("cnt")
|
||||
)
|
||||
.withRollup(false)
|
||||
.build()
|
||||
)
|
||||
.inputSource(
|
||||
ResourceInputSource.of(
|
||||
NestedDataTestUtils.class.getClassLoader(),
|
||||
NestedDataTestUtils.ARRAY_TYPES_DATA_FILE
|
||||
)
|
||||
)
|
||||
.inputFormat(TestDataBuilder.DEFAULT_JSON_INPUT_FORMAT)
|
||||
.inputTmpDir(new File(tmpDir, "9-input"))
|
||||
.buildMMappedIndex();
|
||||
|
||||
return SpecificSegmentsQuerySegmentWalker.createWalker(
|
||||
injector,
|
||||
conglomerate,
|
||||
|
|
@ -946,6 +971,15 @@ public class TestDataBuilder
|
|||
.size(0)
|
||||
.build(),
|
||||
makeWikipediaIndexWithAggregation(tmpDir)
|
||||
).add(
|
||||
DataSegment.builder()
|
||||
.dataSource(CalciteTests.ARRAYS_DATASOURCE)
|
||||
.version("1")
|
||||
.interval(arraysIndex.getDataInterval())
|
||||
.shardSpec(new LinearShardSpec(1))
|
||||
.size(0)
|
||||
.build(),
|
||||
arraysIndex
|
||||
);
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue