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new 85d36be085 Information schema now uses numeric column types (#13777)
85d36be085 is described below
commit 85d36be0853b6eaa5f2f94be3028a91291ed56b3
Author: Paul Rogers <[email protected]>
AuthorDate: Fri Feb 17 14:39:31 2023 -0800
Information schema now uses numeric column types (#13777)
Change to use SQL schemas to allow null numeric columns
* Updated docs
---
docs/querying/sql-metadata-tables.md | 174 +++++++++---------
.../sql/calcite/schema/InformationSchema.java | 203 ++++++++++++---------
2 files changed, 199 insertions(+), 178 deletions(-)
diff --git a/docs/querying/sql-metadata-tables.md
b/docs/querying/sql-metadata-tables.md
index 2373062870..bd1c1489d5 100644
--- a/docs/querying/sql-metadata-tables.md
+++ b/docs/querying/sql-metadata-tables.md
@@ -53,50 +53,50 @@ WHERE "TABLE_SCHEMA" = 'druid' AND "TABLE_NAME" = 'foo'
### SCHEMATA table
`INFORMATION_SCHEMA.SCHEMATA` provides a list of all known schemas, which
include `druid` for standard [Druid Table datasources](datasource.md#table),
`lookup` for [Lookups](datasource.md#lookup), `sys` for the virtual [System
metadata tables](#system-schema), and `INFORMATION_SCHEMA` for these virtual
tables. Tables are allowed to have the same name across different schemas, so
the schema may be included in an SQL statement to distinguish them, e.g.
`lookup.table` vs `druid.table`.
-|Column|Notes|
-|------|-----|
-|CATALOG_NAME|Always set as `druid`|
-|SCHEMA_NAME|`druid`, `lookup`, `sys`, or `INFORMATION_SCHEMA`|
-|SCHEMA_OWNER|Unused|
-|DEFAULT_CHARACTER_SET_CATALOG|Unused|
-|DEFAULT_CHARACTER_SET_SCHEMA|Unused|
-|DEFAULT_CHARACTER_SET_NAME|Unused|
-|SQL_PATH|Unused|
+|Column|Type|Notes|
+|------|----|-----|
+|CATALOG_NAME|VARCHAR|Always set as `druid`|
+|SCHEMA_NAME|VARCHAR|`druid`, `lookup`, `sys`, or `INFORMATION_SCHEMA`|
+|SCHEMA_OWNER|VARCHAR|Unused|
+|DEFAULT_CHARACTER_SET_CATALOG|VARCHAR|Unused|
+|DEFAULT_CHARACTER_SET_SCHEMA|VARCHAR|Unused|
+|DEFAULT_CHARACTER_SET_NAME|VARCHAR|Unused|
+|SQL_PATH|VARCHAR|Unused|
### TABLES table
`INFORMATION_SCHEMA.TABLES` provides a list of all known tables and schemas.
-|Column|Notes|
-|------|-----|
-|TABLE_CATALOG|Always set as `druid`|
-|TABLE_SCHEMA|The 'schema' which the table falls under, see [SCHEMATA table
for details](#schemata-table)|
-|TABLE_NAME|Table name. For the `druid` schema, this is the `dataSource`.|
-|TABLE_TYPE|"TABLE" or "SYSTEM_TABLE"|
-|IS_JOINABLE|If a table is directly joinable if on the right hand side of a
`JOIN` statement, without performing a subquery, this value will be set to
`YES`, otherwise `NO`. Lookups are always joinable because they are globally
distributed among Druid query processing nodes, but Druid datasources are not,
and will use a less efficient subquery join.|
-|IS_BROADCAST|If a table is 'broadcast' and distributed among all Druid query
processing nodes, this value will be set to `YES`, such as lookups and Druid
datasources which have a 'broadcast' load rule, else `NO`.|
+|Column|Type|Notes|
+|------|----|-----|
+|TABLE_CATALOG|VARCHAR|Always set as `druid`|
+|TABLE_SCHEMA|VARCHAR|The 'schema' which the table falls under, see [SCHEMATA
table for details](#schemata-table)|
+|TABLE_NAME|VARCHAR|Table name. For the `druid` schema, this is the
`dataSource`.|
+|TABLE_TYPE|VARCHAR|"TABLE" or "SYSTEM_TABLE"|
+|IS_JOINABLE|VARCHAR|If a table is directly joinable if on the right hand side
of a `JOIN` statement, without performing a subquery, this value will be set to
`YES`, otherwise `NO`. Lookups are always joinable because they are globally
distributed among Druid query processing nodes, but Druid datasources are not,
and will use a less efficient subquery join.|
+|IS_BROADCAST|VARCHAR|If a table is 'broadcast' and distributed among all
Druid query processing nodes, this value will be set to `YES`, such as lookups
and Druid datasources which have a 'broadcast' load rule, else `NO`.|
### COLUMNS table
`INFORMATION_SCHEMA.COLUMNS` provides a list of all known columns across all
tables and schema.
-|Column|Notes|
-|------|-----|
-|TABLE_CATALOG|Always set as `druid`|
-|TABLE_SCHEMA|The 'schema' which the table column falls under, see [SCHEMATA
table for details](#schemata-table)|
-|TABLE_NAME|The 'table' which the column belongs to, see [TABLES table for
details](#tables-table)|
-|COLUMN_NAME|The column name|
-|ORDINAL_POSITION|The order in which the column is stored in a table|
-|COLUMN_DEFAULT|Unused|
-|IS_NULLABLE||
-|DATA_TYPE||
-|CHARACTER_MAXIMUM_LENGTH|Unused|
-|CHARACTER_OCTET_LENGTH|Unused|
-|NUMERIC_PRECISION||
-|NUMERIC_PRECISION_RADIX||
-|NUMERIC_SCALE||
-|DATETIME_PRECISION||
-|CHARACTER_SET_NAME||
-|COLLATION_NAME||
-|JDBC_TYPE|Type code from java.sql.Types (Druid extension)|
+|Column|Type|Notes|
+|------|----|-----|
+|TABLE_CATALOG|VARCHAR|Always set as `druid`|
+|TABLE_SCHEMA|VARCHAR|The 'schema' which the table column falls under, see
[SCHEMATA table for details](#schemata-table)|
+|TABLE_NAME|VARCHAR|The 'table' which the column belongs to, see [TABLES table
for details](#tables-table)|
+|COLUMN_NAME|VARCHAR|The column name|
+|ORDINAL_POSITION|BIGINT|The order in which the column is stored in a table|
+|COLUMN_DEFAULT|VARCHAR|Unused|
+|IS_NULLABLE|VARCHAR||
+|DATA_TYPE|VARCHAR||
+|CHARACTER_MAXIMUM_LENGTH|BIGINT|Unused|
+|CHARACTER_OCTET_LENGTH|BIGINT|Unused|
+|NUMERIC_PRECISION|BIGINT||
+|NUMERIC_PRECISION_RADIX|BIGINT||
+|NUMERIC_SCALE|BIGINT||
+|DATETIME_PRECISION|BIGINT||
+|CHARACTER_SET_NAME|VARCHAR||
+|COLLATION_NAME|VARCHAR||
+|JDBC_TYPE|BIGINT|Type code from java.sql.Types (Druid extension)|
For example, this query returns [data type](sql-data-types.md) information for
columns in the `foo` table:
@@ -119,24 +119,24 @@ Segments table provides details on all Druid segments,
whether they are publishe
|Column|Type|Notes|
|------|-----|-----|
-|segment_id|STRING|Unique segment identifier|
-|datasource|STRING|Name of datasource|
-|start|STRING|Interval start time (in ISO 8601 format)|
-|end|STRING|Interval end time (in ISO 8601 format)|
-|size|LONG|Size of segment in bytes|
-|version|STRING|Version string (generally an ISO8601 timestamp corresponding
to when the segment set was first started). Higher version means the more
recently created segment. Version comparing is based on string comparison.|
-|partition_num|LONG|Partition number (an integer, unique within a
datasource+interval+version; may not necessarily be contiguous)|
-|num_replicas|LONG|Number of replicas of this segment currently being served|
-|num_rows|LONG|Number of rows in this segment, or zero if the number of rows
is not known.<br /><br />This row count is gathered by the Broker in the
background. It will be zero if the Broker has not gathered a row count for this
segment yet. For segments ingested from streams, the reported row count may lag
behind the result of a `count(*)` query because the cached `num_rows` on the
Broker may be out of date. This will settle shortly after new rows stop being
written to that particular [...]
-|is_active|LONG|True for segments that represent the latest state of a
datasource.<br /><br />Equivalent to `(is_published = 1 AND is_overshadowed =
0) OR is_realtime = 1`. In steady state, when no ingestion or data management
operations are happening, `is_active` will be equivalent to `is_available`.
However, they may differ from each other when ingestion or data management
operations have executed recently. In these cases, Druid will load and unload
segments appropriately to bring actu [...]
-|is_published|LONG|Boolean represented as long type where 1 = true, 0 = false.
1 if this segment has been published to the metadata store and is marked as
used. See the [segment lifecycle
documentation](../design/architecture.md#segment-lifecycle) for more details.|
-|is_available|LONG|Boolean represented as long type where 1 = true, 0 = false.
1 if this segment is currently being served by any data serving process, like a
Historical or a realtime ingestion task. See the [segment lifecycle
documentation](../design/architecture.md#segment-lifecycle) for more details.|
-|is_realtime|LONG|Boolean represented as long type where 1 = true, 0 = false.
1 if this segment is _only_ served by realtime tasks, and 0 if any Historical
process is serving this segment.|
-|is_overshadowed|LONG|Boolean represented as long type where 1 = true, 0 =
false. 1 if this segment is published and is _fully_ overshadowed by some other
published segments. Currently, `is_overshadowed` is always 0 for unpublished
segments, although this may change in the future. You can filter for segments
that "should be published" by filtering for `is_published = 1 AND
is_overshadowed = 0`. Segments can briefly be both published and overshadowed
if they were recently replaced, but ha [...]
-|shard_spec|STRING|JSON-serialized form of the segment `ShardSpec`|
-|dimensions|STRING|JSON-serialized form of the segment dimensions|
-|metrics|STRING|JSON-serialized form of the segment metrics|
-|last_compaction_state|STRING|JSON-serialized form of the compaction task's
config (compaction task which created this segment). May be null if segment was
not created by compaction task.|
+|segment_id|VARCHAR|Unique segment identifier|
+|datasource|VARCHAR|Name of datasource|
+|start|VARCHAR|Interval start time (in ISO 8601 format)|
+|end|VARCHAR|Interval end time (in ISO 8601 format)|
+|size|BIGINT|Size of segment in bytes|
+|version|VARCHAR|Version string (generally an ISO8601 timestamp corresponding
to when the segment set was first started). Higher version means the more
recently created segment. Version comparing is based on string comparison.|
+|partition_num|BIGINT|Partition number (an integer, unique within a
datasource+interval+version; may not necessarily be contiguous)|
+|num_replicas|BIGINT|Number of replicas of this segment currently being served|
+|num_rows|BIGINT|Number of rows in this segment, or zero if the number of rows
is not known.<br /><br />This row count is gathered by the Broker in the
background. It will be zero if the Broker has not gathered a row count for this
segment yet. For segments ingested from streams, the reported row count may lag
behind the result of a `count(*)` query because the cached `num_rows` on the
Broker may be out of date. This will settle shortly after new rows stop being
written to that particula [...]
+|is_active|BIGINT|True for segments that represent the latest state of a
datasource.<br /><br />Equivalent to `(is_published = 1 AND is_overshadowed =
0) OR is_realtime = 1`. In steady state, when no ingestion or data management
operations are happening, `is_active` will be equivalent to `is_available`.
However, they may differ from each other when ingestion or data management
operations have executed recently. In these cases, Druid will load and unload
segments appropriately to bring ac [...]
+|is_published|BIGINT|Boolean represented as long type where 1 = true, 0 =
false. 1 if this segment has been published to the metadata store and is marked
as used. See the [segment lifecycle
documentation](../design/architecture.md#segment-lifecycle) for more details.|
+|is_available|BIGINT|Boolean represented as long type where 1 = true, 0 =
false. 1 if this segment is currently being served by any data serving process,
like a Historical or a realtime ingestion task. See the [segment lifecycle
documentation](../design/architecture.md#segment-lifecycle) for more details.|
+|is_realtime|BIGINT|Boolean represented as long type where 1 = true, 0 =
false. 1 if this segment is _only_ served by realtime tasks, and 0 if any
Historical process is serving this segment.|
+|is_overshadowed|BIGINT|Boolean represented as long type where 1 = true, 0 =
false. 1 if this segment is published and is _fully_ overshadowed by some other
published segments. Currently, `is_overshadowed` is always 0 for unpublished
segments, although this may change in the future. You can filter for segments
that "should be published" by filtering for `is_published = 1 AND
is_overshadowed = 0`. Segments can briefly be both published and overshadowed
if they were recently replaced, but [...]
+|shard_spec|VARCHAR|JSON-serialized form of the segment `ShardSpec`|
+|dimensions|VARCHAR|JSON-serialized form of the segment dimensions|
+|metrics|VARCHAR|JSON-serialized form of the segment metrics|
+|last_compaction_state|VARCHAR|JSON-serialized form of the compaction task's
config (compaction task which created this segment). May be null if segment was
not created by compaction task.|
For example, to retrieve all currently active segments for datasource
"wikipedia", use the query:
@@ -199,15 +199,15 @@ Servers table lists all discovered servers in the cluster.
|Column|Type|Notes|
|------|-----|-----|
-|server|STRING|Server name in the form host:port|
-|host|STRING|Hostname of the server|
-|plaintext_port|LONG|Unsecured port of the server, or -1 if plaintext traffic
is disabled|
-|tls_port|LONG|TLS port of the server, or -1 if TLS is disabled|
-|server_type|STRING|Type of Druid service. Possible values include:
COORDINATOR, OVERLORD, BROKER, ROUTER, HISTORICAL, MIDDLE_MANAGER or PEON.|
-|tier|STRING|Distribution tier see
[druid.server.tier](../configuration/index.md#historical-general-configuration).
Only valid for HISTORICAL type, for other types it's null|
-|current_size|LONG|Current size of segments in bytes on this server. Only
valid for HISTORICAL type, for other types it's 0|
-|max_size|LONG|Max size in bytes this server recommends to assign to segments
see
[druid.server.maxSize](../configuration/index.md#historical-general-configuration).
Only valid for HISTORICAL type, for other types it's 0|
-|is_leader|LONG|1 if the server is currently the 'leader' (for services which
have the concept of leadership), otherwise 0 if the server is not the leader,
or the default long value (0 or null depending on
`druid.generic.useDefaultValueForNull`) if the server type does not have the
concept of leadership|
+|server|VARCHAR|Server name in the form host:port|
+|host|VARCHAR|Hostname of the server|
+|plaintext_port|BIGINT|Unsecured port of the server, or -1 if plaintext
traffic is disabled|
+|tls_port|BIGINT|TLS port of the server, or -1 if TLS is disabled|
+|server_type|VARCHAR|Type of Druid service. Possible values include:
COORDINATOR, OVERLORD, BROKER, ROUTER, HISTORICAL, MIDDLE_MANAGER or PEON.|
+|tier|VARCHAR|Distribution tier see
[druid.server.tier](../configuration/index.md#historical-general-configuration).
Only valid for HISTORICAL type, for other types it's null|
+|current_size|BIGINT|Current size of segments in bytes on this server. Only
valid for HISTORICAL type, for other types it's 0|
+|max_size|BIGINT|Max size in bytes this server recommends to assign to
segments see
[druid.server.maxSize](../configuration/index.md#historical-general-configuration).
Only valid for HISTORICAL type, for other types it's 0|
+|is_leader|BIGINT|1 if the server is currently the 'leader' (for services
which have the concept of leadership), otherwise 0 if the server is not the
leader, or the default long value (0 or null depending on
`druid.generic.useDefaultValueForNull`) if the server type does not have the
concept of leadership|
To retrieve information about all servers, use the query:
@@ -221,8 +221,8 @@ SERVER_SEGMENTS is used to join servers with segments table
|Column|Type|Notes|
|------|-----|-----|
-|server|STRING|Server name in format host:port (Primary key of [servers
table](#servers-table))|
-|segment_id|STRING|Segment identifier (Primary key of [segments
table](#segments-table))|
+|server|VARCHAR|Server name in format host:port (Primary key of [servers
table](#servers-table))|
+|segment_id|VARCHAR|Segment identifier (Primary key of [segments
table](#segments-table))|
JOIN between "servers" and "segments" can be used to query the number of
segments for a specific datasource,
grouped by server, example query:
@@ -244,20 +244,20 @@ check out the documentation for [ingestion
tasks](../ingestion/tasks.md).
|Column|Type|Notes|
|------|-----|-----|
-|task_id|STRING|Unique task identifier|
-|group_id|STRING|Task group ID for this task, the value depends on the task
`type`. For example, for native index tasks, it's same as `task_id`, for sub
tasks, this value is the parent task's ID|
-|type|STRING|Task type, for example this value is "index" for indexing tasks.
See [tasks-overview](../ingestion/tasks.md)|
-|datasource|STRING|Datasource name being indexed|
-|created_time|STRING|Timestamp in ISO8601 format corresponding to when the
ingestion task was created. Note that this value is populated for completed and
waiting tasks. For running and pending tasks this value is set to
1970-01-01T00:00:00Z|
-|queue_insertion_time|STRING|Timestamp in ISO8601 format corresponding to when
this task was added to the queue on the Overlord|
-|status|STRING|Status of a task can be RUNNING, FAILED, SUCCESS|
-|runner_status|STRING|Runner status of a completed task would be NONE, for
in-progress tasks this can be RUNNING, WAITING, PENDING|
-|duration|LONG|Time it took to finish the task in milliseconds, this value is
present only for completed tasks|
-|location|STRING|Server name where this task is running in the format
host:port, this information is present only for RUNNING tasks|
-|host|STRING|Hostname of the server where task is running|
-|plaintext_port|LONG|Unsecured port of the server, or -1 if plaintext traffic
is disabled|
-|tls_port|LONG|TLS port of the server, or -1 if TLS is disabled|
-|error_msg|STRING|Detailed error message in case of FAILED tasks|
+|task_id|VARCHAR|Unique task identifier|
+|group_id|VARCHAR|Task group ID for this task, the value depends on the task
`type`. For example, for native index tasks, it's same as `task_id`, for sub
tasks, this value is the parent task's ID|
+|type|VARCHAR|Task type, for example this value is "index" for indexing tasks.
See [tasks-overview](../ingestion/tasks.md)|
+|datasource|VARCHAR|Datasource name being indexed|
+|created_time|VARCHAR|Timestamp in ISO8601 format corresponding to when the
ingestion task was created. Note that this value is populated for completed and
waiting tasks. For running and pending tasks this value is set to
1970-01-01T00:00:00Z|
+|queue_insertion_time|VARCHAR|Timestamp in ISO8601 format corresponding to
when this task was added to the queue on the Overlord|
+|status|VARCHAR|Status of a task can be RUNNING, FAILED, SUCCESS|
+|runner_status|VARCHAR|Runner status of a completed task would be NONE, for
in-progress tasks this can be RUNNING, WAITING, PENDING|
+|duration|BIGINT|Time it took to finish the task in milliseconds, this value
is present only for completed tasks|
+|location|VARCHAR|Server name where this task is running in the format
host:port, this information is present only for RUNNING tasks|
+|host|VARCHAR|Hostname of the server where task is running|
+|plaintext_port|BIGINT|Unsecured port of the server, or -1 if plaintext
traffic is disabled|
+|tls_port|BIGINT|TLS port of the server, or -1 if TLS is disabled|
+|error_msg|VARCHAR|Detailed error message in case of FAILED tasks|
For example, to retrieve tasks information filtered by status, use the query
@@ -271,14 +271,14 @@ The supervisors table provides information about
supervisors.
|Column|Type|Notes|
|------|-----|-----|
-|supervisor_id|STRING|Supervisor task identifier|
-|state|STRING|Basic state of the supervisor. Available states:
`UNHEALTHY_SUPERVISOR`, `UNHEALTHY_TASKS`, `PENDING`, `RUNNING`, `SUSPENDED`,
`STOPPING`. Check [Kafka
Docs](../development/extensions-core/kafka-supervisor-operations.md) for
details.|
-|detailed_state|STRING|Supervisor specific state. (See documentation of the
specific supervisor for details, e.g.
[Kafka](../development/extensions-core/kafka-ingestion.md) or
[Kinesis](../development/extensions-core/kinesis-ingestion.md))|
-|healthy|LONG|Boolean represented as long type where 1 = true, 0 = false. 1
indicates a healthy supervisor|
-|type|STRING|Type of supervisor, e.g. `kafka`, `kinesis` or
`materialized_view`|
-|source|STRING|Source of the supervisor, e.g. Kafka topic or Kinesis stream|
-|suspended|LONG|Boolean represented as long type where 1 = true, 0 = false. 1
indicates supervisor is in suspended state|
-|spec|STRING|JSON-serialized supervisor spec|
+|supervisor_id|VARCHAR|Supervisor task identifier|
+|state|VARCHAR|Basic state of the supervisor. Available states:
`UNHEALTHY_SUPERVISOR`, `UNHEALTHY_TASKS`, `PENDING`, `RUNNING`, `SUSPENDED`,
`STOPPING`. Check [Kafka
Docs](../development/extensions-core/kafka-supervisor-operations.md) for
details.|
+|detailed_state|VARCHAR|Supervisor specific state. (See documentation of the
specific supervisor for details, e.g.
[Kafka](../development/extensions-core/kafka-ingestion.md) or
[Kinesis](../development/extensions-core/kinesis-ingestion.md))|
+|healthy|BIGINT|Boolean represented as long type where 1 = true, 0 = false. 1
indicates a healthy supervisor|
+|type|VARCHAR|Type of supervisor, e.g. `kafka`, `kinesis` or
`materialized_view`|
+|source|VARCHAR|Source of the supervisor, e.g. Kafka topic or Kinesis stream|
+|suspended|BIGINT|Boolean represented as long type where 1 = true, 0 = false.
1 indicates supervisor is in suspended state|
+|spec|VARCHAR|JSON-serialized supervisor spec|
For example, to retrieve supervisor tasks information filtered by health
status, use the query
diff --git
a/sql/src/main/java/org/apache/druid/sql/calcite/schema/InformationSchema.java
b/sql/src/main/java/org/apache/druid/sql/calcite/schema/InformationSchema.java
index 7f16d0ae9f..2e46406cdf 100644
---
a/sql/src/main/java/org/apache/druid/sql/calcite/schema/InformationSchema.java
+++
b/sql/src/main/java/org/apache/druid/sql/calcite/schema/InformationSchema.java
@@ -46,14 +46,14 @@ import org.apache.calcite.schema.impl.AbstractSchema;
import org.apache.calcite.schema.impl.AbstractTable;
import org.apache.calcite.sql.type.SqlTypeName;
import org.apache.druid.java.util.emitter.EmittingLogger;
-import org.apache.druid.segment.column.ColumnType;
-import org.apache.druid.segment.column.RowSignature;
import org.apache.druid.server.security.Action;
import org.apache.druid.server.security.AuthenticationResult;
import org.apache.druid.server.security.AuthorizationUtils;
import org.apache.druid.server.security.AuthorizerMapper;
import org.apache.druid.server.security.Resource;
import org.apache.druid.server.security.ResourceAction;
+import org.apache.druid.sql.calcite.planner.Calcites;
+import org.apache.druid.sql.calcite.planner.DruidTypeSystem;
import org.apache.druid.sql.calcite.planner.PlannerContext;
import org.apache.druid.sql.calcite.table.DruidTable;
import org.apache.druid.sql.calcite.table.RowSignatures;
@@ -73,44 +73,65 @@ public class InformationSchema extends AbstractSchema
private static final String SCHEMATA_TABLE = "SCHEMATA";
private static final String TABLES_TABLE = "TABLES";
private static final String COLUMNS_TABLE = "COLUMNS";
- private static final RowSignature SCHEMATA_SIGNATURE = RowSignature
- .builder()
- .add("CATALOG_NAME", ColumnType.STRING)
- .add("SCHEMA_NAME", ColumnType.STRING)
- .add("SCHEMA_OWNER", ColumnType.STRING)
- .add("DEFAULT_CHARACTER_SET_CATALOG", ColumnType.STRING)
- .add("DEFAULT_CHARACTER_SET_SCHEMA", ColumnType.STRING)
- .add("DEFAULT_CHARACTER_SET_NAME", ColumnType.STRING)
- .add("SQL_PATH", ColumnType.STRING)
+
+ private static class RowTypeBuilder
+ {
+ final RelDataTypeFactory typeFactory = DruidTypeSystem.TYPE_FACTORY;
+ final RelDataTypeFactory.Builder builder = typeFactory.builder();
+
+ public RowTypeBuilder add(String name, SqlTypeName type)
+ {
+ builder.add(name, Calcites.createSqlTypeWithNullability(typeFactory,
type, false));
+ return this;
+ }
+
+ public RowTypeBuilder add(String name, SqlTypeName type, boolean nullable)
+ {
+ builder.add(name, Calcites.createSqlTypeWithNullability(typeFactory,
type, nullable));
+ return this;
+ }
+
+ public RelDataType build()
+ {
+ return builder.build();
+ }
+ }
+
+ private static final RelDataType SCHEMATA_SIGNATURE = new RowTypeBuilder()
+ .add("CATALOG_NAME", SqlTypeName.VARCHAR)
+ .add("SCHEMA_NAME", SqlTypeName.VARCHAR)
+ .add("SCHEMA_OWNER", SqlTypeName.VARCHAR)
+ .add("DEFAULT_CHARACTER_SET_CATALOG", SqlTypeName.VARCHAR)
+ .add("DEFAULT_CHARACTER_SET_SCHEMA", SqlTypeName.VARCHAR)
+ .add("DEFAULT_CHARACTER_SET_NAME", SqlTypeName.VARCHAR)
+ .add("SQL_PATH", SqlTypeName.VARCHAR)
.build();
- private static final RowSignature TABLES_SIGNATURE = RowSignature
- .builder()
- .add("TABLE_CATALOG", ColumnType.STRING)
- .add("TABLE_SCHEMA", ColumnType.STRING)
- .add("TABLE_NAME", ColumnType.STRING)
- .add("TABLE_TYPE", ColumnType.STRING)
- .add("IS_JOINABLE", ColumnType.STRING)
- .add("IS_BROADCAST", ColumnType.STRING)
+ private static final RelDataType TABLES_SIGNATURE = new RowTypeBuilder()
+ .add("TABLE_CATALOG", SqlTypeName.VARCHAR)
+ .add("TABLE_SCHEMA", SqlTypeName.VARCHAR)
+ .add("TABLE_NAME", SqlTypeName.VARCHAR)
+ .add("TABLE_TYPE", SqlTypeName.VARCHAR)
+ .add("IS_JOINABLE", SqlTypeName.VARCHAR)
+ .add("IS_BROADCAST", SqlTypeName.VARCHAR)
.build();
- private static final RowSignature COLUMNS_SIGNATURE = RowSignature
- .builder()
- .add("TABLE_CATALOG", ColumnType.STRING)
- .add("TABLE_SCHEMA", ColumnType.STRING)
- .add("TABLE_NAME", ColumnType.STRING)
- .add("COLUMN_NAME", ColumnType.STRING)
- .add("ORDINAL_POSITION", ColumnType.STRING)
- .add("COLUMN_DEFAULT", ColumnType.STRING)
- .add("IS_NULLABLE", ColumnType.STRING)
- .add("DATA_TYPE", ColumnType.STRING)
- .add("CHARACTER_MAXIMUM_LENGTH", ColumnType.STRING)
- .add("CHARACTER_OCTET_LENGTH", ColumnType.STRING)
- .add("NUMERIC_PRECISION", ColumnType.STRING)
- .add("NUMERIC_PRECISION_RADIX", ColumnType.STRING)
- .add("NUMERIC_SCALE", ColumnType.STRING)
- .add("DATETIME_PRECISION", ColumnType.STRING)
- .add("CHARACTER_SET_NAME", ColumnType.STRING)
- .add("COLLATION_NAME", ColumnType.STRING)
- .add("JDBC_TYPE", ColumnType.LONG)
+ private static final RelDataType COLUMNS_SIGNATURE = new RowTypeBuilder()
+ .add("TABLE_CATALOG", SqlTypeName.VARCHAR)
+ .add("TABLE_SCHEMA", SqlTypeName.VARCHAR)
+ .add("TABLE_NAME", SqlTypeName.VARCHAR)
+ .add("COLUMN_NAME", SqlTypeName.VARCHAR)
+ .add("ORDINAL_POSITION", SqlTypeName.BIGINT)
+ .add("COLUMN_DEFAULT", SqlTypeName.VARCHAR)
+ .add("IS_NULLABLE", SqlTypeName.VARCHAR)
+ .add("DATA_TYPE", SqlTypeName.VARCHAR)
+ .add("CHARACTER_MAXIMUM_LENGTH", SqlTypeName.VARCHAR, true)
+ .add("CHARACTER_OCTET_LENGTH", SqlTypeName.VARCHAR, true)
+ .add("NUMERIC_PRECISION", SqlTypeName.BIGINT, true)
+ .add("NUMERIC_PRECISION_RADIX", SqlTypeName.BIGINT, true)
+ .add("NUMERIC_SCALE", SqlTypeName.BIGINT, true)
+ .add("DATETIME_PRECISION", SqlTypeName.BIGINT, true)
+ .add("CHARACTER_SET_NAME", SqlTypeName.VARCHAR, true)
+ .add("COLLATION_NAME", SqlTypeName.VARCHAR, true)
+ .add("JDBC_TYPE", SqlTypeName.BIGINT)
.build();
private static final RelDataTypeSystem TYPE_SYSTEM =
RelDataTypeSystem.DEFAULT;
@@ -175,7 +196,7 @@ public class InformationSchema extends AbstractSchema
@Override
public RelDataType getRowType(final RelDataTypeFactory typeFactory)
{
- return RowSignatures.toRelDataType(SCHEMATA_SIGNATURE, typeFactory);
+ return SCHEMATA_SIGNATURE;
}
@Override
@@ -279,7 +300,7 @@ public class InformationSchema extends AbstractSchema
@Override
public RelDataType getRowType(final RelDataTypeFactory typeFactory)
{
- return RowSignatures.toRelDataType(TABLES_SIGNATURE, typeFactory);
+ return TABLES_SIGNATURE;
}
@Override
@@ -333,10 +354,15 @@ public class InformationSchema extends AbstractSchema
@Override
public Iterable<Object[]> apply(final
String tableName)
{
+ Table table =
subSchema.getTable(tableName);
+ if (table == null) {
+ // Table just disappeared.
+ return null;
+ }
return generateColumnMetadata(
schemaName,
tableName,
- subSchema.getTable(tableName),
+ table.getRowType(typeFactory),
typeFactory
);
}
@@ -349,20 +375,20 @@ public class InformationSchema extends AbstractSchema
public Iterable<Object[]> apply(final
String functionName)
{
final TableMacro viewMacro =
getView(subSchema, functionName);
- if (viewMacro == null) {
- return null;
- }
-
- try {
- return generateColumnMetadata(
- schemaName,
- functionName,
-
viewMacro.apply(Collections.emptyList()),
- typeFactory
- );
- }
- catch (Exception e) {
- log.error(e, "Encountered exception
while handling view[%s].", functionName);
+ if (viewMacro != null) {
+ try {
+ return generateColumnMetadata(
+ schemaName,
+ functionName,
+
viewMacro.apply(Collections.emptyList()).getRowType(typeFactory),
+ typeFactory
+ );
+ }
+ catch (Exception e) {
+ log.error(e, "Encountered exception
while handling view[%s].", functionName);
+ return null;
+ }
+ } else {
return null;
}
}
@@ -382,7 +408,7 @@ public class InformationSchema extends AbstractSchema
@Override
public RelDataType getRowType(final RelDataTypeFactory typeFactory)
{
- return RowSignatures.toRelDataType(COLUMNS_SIGNATURE, typeFactory);
+ return COLUMNS_SIGNATURE;
}
@Override
@@ -401,16 +427,12 @@ public class InformationSchema extends AbstractSchema
private Iterable<Object[]> generateColumnMetadata(
final String schemaName,
final String tableName,
- final Table table,
+ final RelDataType tableSchema,
final RelDataTypeFactory typeFactory
)
{
- if (table == null) {
- return null;
- }
-
return FluentIterable
- .from(table.getRowType(typeFactory).getFieldList())
+ .from(tableSchema.getFieldList())
.transform(
new Function<RelDataTypeField, Object[]>()
{
@@ -418,29 +440,30 @@ public class InformationSchema extends AbstractSchema
public Object[] apply(final RelDataTypeField field)
{
final RelDataType type = field.getType();
- boolean isNumeric =
SqlTypeName.NUMERIC_TYPES.contains(type.getSqlTypeName());
- boolean isCharacter =
SqlTypeName.CHAR_TYPES.contains(type.getSqlTypeName());
- boolean isDateTime =
SqlTypeName.DATETIME_TYPES.contains(type.getSqlTypeName());
+ SqlTypeName sqlTypeName = type.getSqlTypeName();
+ boolean isNumeric =
SqlTypeName.NUMERIC_TYPES.contains(sqlTypeName);
+ boolean isCharacter =
SqlTypeName.CHAR_TYPES.contains(sqlTypeName);
+ boolean isDateTime =
SqlTypeName.DATETIME_TYPES.contains(sqlTypeName);
- final String typeName = type instanceof
RowSignatures.ComplexSqlType ? ((RowSignatures.ComplexSqlType)
type).asTypeString() : type.getSqlTypeName().toString();
+ final String typeName = type instanceof
RowSignatures.ComplexSqlType ? ((RowSignatures.ComplexSqlType)
type).asTypeString() : sqlTypeName.toString();
return new Object[]{
CATALOG_NAME, // TABLE_CATALOG
schemaName, // TABLE_SCHEMA
tableName, // TABLE_NAME
field.getName(), // COLUMN_NAME
- String.valueOf(field.getIndex()), // ORDINAL_POSITION
+ (long) (field.getIndex() + 1), // ORDINAL_POSITION
"", // COLUMN_DEFAULT
type.isNullable() ? INFO_TRUE : INFO_FALSE, //
IS_NULLABLE
typeName, // DATA_TYPE
null, // CHARACTER_MAXIMUM_LENGTH
null, // CHARACTER_OCTET_LENGTH
- isNumeric ? String.valueOf(type.getPrecision()) : null,
// NUMERIC_PRECISION
- isNumeric ? "10" : null, // NUMERIC_PRECISION_RADIX
- isNumeric ? String.valueOf(type.getScale()) : null, //
NUMERIC_SCALE
- isDateTime ? String.valueOf(type.getPrecision()) : null,
// DATETIME_PRECISION
+ isNumeric ? (long) type.getPrecision() : null, //
NUMERIC_PRECISION
+ isNumeric ? 10L : null, // NUMERIC_PRECISION_RADIX
+ isNumeric ? (long) type.getScale() : null, //
NUMERIC_SCALE
+ isDateTime ? (long) type.getPrecision() : null, //
DATETIME_PRECISION
isCharacter ? type.getCharset().name() : null, //
CHARACTER_SET_NAME
isCharacter ? type.getCollation().getCollationName() :
null, // COLLATION_NAME
- Long.valueOf(type.getSqlTypeName().getJdbcOrdinal()) //
JDBC_TYPE (Druid extension)
+ (long) type.getSqlTypeName().getJdbcOrdinal() //
JDBC_TYPE (Druid extension)
};
}
}
@@ -503,27 +526,25 @@ public class InformationSchema extends AbstractSchema
final Set<String> names
)
{
- if (schema != null) {
- return ImmutableSet.copyOf(
- AuthorizationUtils.filterAuthorizedResources(
- authenticationResult,
- names,
- name -> {
- final String resoureType = schema.getSchemaResourceType(name);
- if (resoureType != null) {
- return Collections.singletonList(
- new ResourceAction(new Resource(name, resoureType),
Action.READ)
- );
- } else {
- return Collections.emptyList();
- }
- },
- authorizerMapper
- )
- );
- } else {
+ if (schema == null) {
// for schemas with no resource type, or that are not named schemas, we
don't filter anything
return names;
}
+ return ImmutableSet.copyOf(
+ AuthorizationUtils.filterAuthorizedResources(
+ authenticationResult,
+ names,
+ name -> {
+ final String resourseType = schema.getSchemaResourceType(name);
+ if (resourseType == null) {
+ return Collections.emptyList();
+ }
+ return Collections.singletonList(
+ new ResourceAction(new Resource(name, resourseType),
Action.READ)
+ );
+ },
+ authorizerMapper
+ )
+ );
}
}
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