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25 Commits

Author SHA1 Message Date
Pavel Kruglov
62045ccec2
Merge 7aadca13ba into 44b4bd38b9 2024-11-20 15:25:26 -08:00
Mikhail Artemenko
44b4bd38b9
Merge pull request #72045 from ClickHouse/issues/70174/cluster_versions
Enable cluster table functions for DataLake Storages
2024-11-20 21:22:37 +00:00
Shichao Jin
40c7d5fd1a
Merge pull request #71894 from udiz/fix-arrayWithConstant-size-estimation
Fix: arrayWithConstant size estimation using row's element size
2024-11-20 19:56:27 +00:00
avogar
7aadca13ba Merge branch 'master' of github.com:ClickHouse/ClickHouse into object-to-json-alter 2024-11-20 14:20:34 +00:00
Mikhail Artemenko
4ccebd9a24 fix syntax for iceberg in docs 2024-11-20 11:15:39 +00:00
Mikhail Artemenko
99177c0daf remove icebergCluster alias 2024-11-20 11:15:12 +00:00
Mikhail Artemenko
0951991c1d update aspell-dict.txt 2024-11-19 13:10:42 +00:00
Mikhail Artemenko
19aec5e572 Merge branch 'issues/70174/cluster_versions' of github.com:ClickHouse/ClickHouse into issues/70174/cluster_versions 2024-11-19 12:51:56 +00:00
Mikhail Artemenko
a367de9977 add docs 2024-11-19 12:49:59 +00:00
Mikhail Artemenko
6894e280b2 fix pr issues 2024-11-19 12:34:42 +00:00
Mikhail Artemenko
39ebe113d9 Merge branch 'master' into issues/70174/cluster_versions 2024-11-19 11:28:46 +00:00
udiz
239bbaa133 use length 2024-11-19 00:00:43 +00:00
udiz
07fac5808d format null on test 2024-11-18 23:08:48 +00:00
udiz
ed95e0781f test uses less memory 2024-11-18 22:48:38 +00:00
robot-clickhouse
014608fb6b Automatic style fix 2024-11-18 17:51:51 +00:00
Mikhail Artemenko
a29ded4941 add test for iceberg 2024-11-18 17:39:46 +00:00
Mikhail Artemenko
d2efae7511 enable cluster versions for datalake storages 2024-11-18 17:35:21 +00:00
udiz
6879aa130a newline 2024-11-13 22:47:54 +00:00
udiz
43f3c886a2 add test 2024-11-13 22:46:36 +00:00
udiz
c383a743f7 arrayWithConstant size estimation using single value size 2024-11-13 20:02:31 +00:00
avogar
e7f708696f Fix tests 2024-11-13 13:26:16 +00:00
avogar
725344359d Update tests 2024-11-12 13:23:00 +00:00
avogar
6ac3631d4b Update tests 2024-11-11 20:36:16 +00:00
avogar
1fe395bcd6 Fix test 2024-11-11 20:34:43 +00:00
avogar
b2c77df001 Support ALTER from Object to JSON 2024-11-11 20:27:34 +00:00
36 changed files with 754 additions and 61 deletions

View File

@ -49,4 +49,4 @@ LIMIT 2
**See Also**
- [DeltaLake engine](/docs/en/engines/table-engines/integrations/deltalake.md)
- [DeltaLake cluster table function](/docs/en/sql-reference/table-functions/deltalakeCluster.md)

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@ -0,0 +1,30 @@
---
slug: /en/sql-reference/table-functions/deltalakeCluster
sidebar_position: 46
sidebar_label: deltaLakeCluster
title: "deltaLakeCluster Table Function"
---
This is an extension to the [deltaLake](/docs/en/sql-reference/table-functions/deltalake.md) table function.
Allows processing files from [Delta Lake](https://github.com/delta-io/delta) tables in Amazon S3 in parallel from many nodes in a specified cluster. On initiator it creates a connection to all nodes in the cluster and dispatches each file dynamically. On the worker node it asks the initiator about the next task to process and processes it. This is repeated until all tasks are finished.
**Syntax**
``` sql
deltaLakeCluster(cluster_name, url [,aws_access_key_id, aws_secret_access_key] [,format] [,structure] [,compression])
```
**Arguments**
- `cluster_name` — Name of a cluster that is used to build a set of addresses and connection parameters to remote and local servers.
- Description of all other arguments coincides with description of arguments in equivalent [deltaLake](/docs/en/sql-reference/table-functions/deltalake.md) table function.
**Returned value**
A table with the specified structure for reading data from cluster in the specified Delta Lake table in S3.
**See Also**
- [deltaLake engine](/docs/en/engines/table-engines/integrations/deltalake.md)
- [deltaLake table function](/docs/en/sql-reference/table-functions/deltalake.md)

View File

@ -29,4 +29,4 @@ A table with the specified structure for reading data in the specified Hudi tabl
**See Also**
- [Hudi engine](/docs/en/engines/table-engines/integrations/hudi.md)
- [Hudi cluster table function](/docs/en/sql-reference/table-functions/hudiCluster.md)

View File

@ -0,0 +1,30 @@
---
slug: /en/sql-reference/table-functions/hudiCluster
sidebar_position: 86
sidebar_label: hudiCluster
title: "hudiCluster Table Function"
---
This is an extension to the [hudi](/docs/en/sql-reference/table-functions/hudi.md) table function.
Allows processing files from Apache [Hudi](https://hudi.apache.org/) tables in Amazon S3 in parallel from many nodes in a specified cluster. On initiator it creates a connection to all nodes in the cluster and dispatches each file dynamically. On the worker node it asks the initiator about the next task to process and processes it. This is repeated until all tasks are finished.
**Syntax**
``` sql
hudiCluster(cluster_name, url [,aws_access_key_id, aws_secret_access_key] [,format] [,structure] [,compression])
```
**Arguments**
- `cluster_name` — Name of a cluster that is used to build a set of addresses and connection parameters to remote and local servers.
- Description of all other arguments coincides with description of arguments in equivalent [hudi](/docs/en/sql-reference/table-functions/hudi.md) table function.
**Returned value**
A table with the specified structure for reading data from cluster in the specified Hudi table in S3.
**See Also**
- [Hudi engine](/docs/en/engines/table-engines/integrations/hudi.md)
- [Hudi table function](/docs/en/sql-reference/table-functions/hudi.md)

View File

@ -72,3 +72,4 @@ Table function `iceberg` is an alias to `icebergS3` now.
**See Also**
- [Iceberg engine](/docs/en/engines/table-engines/integrations/iceberg.md)
- [Iceberg cluster table function](/docs/en/sql-reference/table-functions/icebergCluster.md)

View File

@ -0,0 +1,43 @@
---
slug: /en/sql-reference/table-functions/icebergCluster
sidebar_position: 91
sidebar_label: icebergCluster
title: "icebergCluster Table Function"
---
This is an extension to the [iceberg](/docs/en/sql-reference/table-functions/iceberg.md) table function.
Allows processing files from Apache [Iceberg](https://iceberg.apache.org/) in parallel from many nodes in a specified cluster. On initiator it creates a connection to all nodes in the cluster and dispatches each file dynamically. On the worker node it asks the initiator about the next task to process and processes it. This is repeated until all tasks are finished.
**Syntax**
``` sql
icebergS3Cluster(cluster_name, url [, NOSIGN | access_key_id, secret_access_key, [session_token]] [,format] [,compression_method])
icebergS3Cluster(cluster_name, named_collection[, option=value [,..]])
icebergAzureCluster(cluster_name, connection_string|storage_account_url, container_name, blobpath, [,account_name], [,account_key] [,format] [,compression_method])
icebergAzureCluster(cluster_name, named_collection[, option=value [,..]])
icebergHDFSCluster(cluster_name, path_to_table, [,format] [,compression_method])
icebergHDFSCluster(cluster_name, named_collection[, option=value [,..]])
```
**Arguments**
- `cluster_name` — Name of a cluster that is used to build a set of addresses and connection parameters to remote and local servers.
- Description of all other arguments coincides with description of arguments in equivalent [iceberg](/docs/en/sql-reference/table-functions/iceberg.md) table function.
**Returned value**
A table with the specified structure for reading data from cluster in the specified Iceberg table.
**Examples**
```sql
SELECT * FROM icebergS3Cluster('cluster_simple', 'http://test.s3.amazonaws.com/clickhouse-bucket/test_table', 'test', 'test')
```
**See Also**
- [Iceberg engine](/docs/en/engines/table-engines/integrations/iceberg.md)
- [Iceberg table function](/docs/en/sql-reference/table-functions/iceberg.md)

View File

@ -521,7 +521,7 @@ DataTypePtr createConcreteEmptyDynamicColumn(const DataTypePtr & type_in_storage
throw Exception(ErrorCodes::EXPERIMENTAL_FEATURE_ERROR, "Type {} unexpectedly has dynamic columns", type_in_storage->getName());
}
bool hasDynamicSubcolumns(const ColumnsDescription & columns)
bool hasDynamicSubcolumnsDeprecated(const ColumnsDescription & columns)
{
return std::any_of(columns.begin(), columns.end(),
[](const auto & column)

View File

@ -63,7 +63,7 @@ DataTypePtr createConcreteEmptyDynamicColumn(const DataTypePtr & type_in_storage
void extendObjectColumns(NamesAndTypesList & columns_list, const ColumnsDescription & object_columns, bool with_subcolumns);
/// Checks whether @columns contain any column with dynamic subcolumns.
bool hasDynamicSubcolumns(const ColumnsDescription & columns);
bool hasDynamicSubcolumnsDeprecated(const ColumnsDescription & columns);
/// Updates types of objects in @object_columns inplace
/// according to types in new_columns.

View File

@ -220,7 +220,7 @@ struct FormatSettings
bool escape_forward_slashes = true;
bool read_named_tuples_as_objects = false;
bool use_string_type_for_ambiguous_paths_in_named_tuples_inference_from_objects = false;
bool write_named_tuples_as_objects = false;
bool write_named_tuples_as_objects = true;
bool skip_null_value_in_named_tuples = false;
bool defaults_for_missing_elements_in_named_tuple = false;
bool ignore_unknown_keys_in_named_tuple = false;

View File

@ -4108,6 +4108,7 @@ private:
ColumnStringHelpers::WriteHelper write_helper(assert_cast<ColumnString &>(*json_string), input_rows_count);
auto & write_buffer = write_helper.getWriteBuffer();
FormatSettings format_settings = context ? getFormatSettings(context) : FormatSettings{};
format_settings.json.quote_64bit_integers = false;
auto serialization = arguments[0].type->getDefaultSerialization();
for (size_t i = 0; i < input_rows_count; ++i)
{

View File

@ -62,16 +62,17 @@ public:
for (size_t i = 0; i < num_rows; ++i)
{
auto array_size = col_num->getInt(i);
auto element_size = col_value->byteSizeAt(i);
if (unlikely(array_size < 0))
throw Exception(ErrorCodes::TOO_LARGE_ARRAY_SIZE, "Array size {} cannot be negative: while executing function {}", array_size, getName());
Int64 estimated_size = 0;
if (unlikely(common::mulOverflow(array_size, col_value->byteSize(), estimated_size)))
throw Exception(ErrorCodes::TOO_LARGE_ARRAY_SIZE, "Array size {} with element size {} bytes is too large: while executing function {}", array_size, col_value->byteSize(), getName());
if (unlikely(common::mulOverflow(array_size, element_size, estimated_size)))
throw Exception(ErrorCodes::TOO_LARGE_ARRAY_SIZE, "Array size {} with element size {} bytes is too large: while executing function {}", array_size, element_size, getName());
if (unlikely(estimated_size > max_array_size_in_columns_bytes))
throw Exception(ErrorCodes::TOO_LARGE_ARRAY_SIZE, "Array size {} with element size {} bytes is too large: while executing function {}", array_size, col_value->byteSize(), getName());
throw Exception(ErrorCodes::TOO_LARGE_ARRAY_SIZE, "Array size {} with element size {} bytes is too large: while executing function {}", array_size, element_size, getName());
offset += array_size;

View File

@ -755,7 +755,7 @@ void executeQueryWithParallelReplicasCustomKey(
}
ColumnsDescriptionByShardNum columns_object;
if (hasDynamicSubcolumns(columns))
if (hasDynamicSubcolumnsDeprecated(columns))
columns_object = getExtendedObjectsOfRemoteTables(*query_info.cluster, storage_id, columns, context);
ClusterProxy::SelectStreamFactory select_stream_factory

View File

@ -1881,7 +1881,7 @@ bool InterpreterCreateQuery::doCreateTable(ASTCreateQuery & create,
validateVirtualColumns(*res);
if (!res->supportsDynamicSubcolumnsDeprecated() && hasDynamicSubcolumns(res->getInMemoryMetadataPtr()->getColumns()) && mode <= LoadingStrictnessLevel::CREATE)
if (!res->supportsDynamicSubcolumnsDeprecated() && hasDynamicSubcolumnsDeprecated(res->getInMemoryMetadataPtr()->getColumns()) && mode <= LoadingStrictnessLevel::CREATE)
{
throw Exception(ErrorCodes::ILLEGAL_COLUMN,
"Cannot create table with column of type Object, "

View File

@ -1450,9 +1450,8 @@ void AlterCommands::validate(const StoragePtr & table, ContextPtr context) const
const auto old_data_type = all_columns.getColumn(options, column_name).type;
bool new_type_has_deprecated_object = command.data_type->hasDynamicSubcolumnsDeprecated();
bool old_type_has_deprecated_object = old_data_type->hasDynamicSubcolumnsDeprecated();
if (new_type_has_deprecated_object || old_type_has_deprecated_object)
if (new_type_has_deprecated_object)
throw Exception(
ErrorCodes::BAD_ARGUMENTS,
"The change of data type {} of column {} to {} is not allowed. It has known bugs",

View File

@ -4104,7 +4104,7 @@ void MergeTreeData::checkPartDynamicColumns(MutableDataPartPtr & part, DataParts
const auto & columns = metadata_snapshot->getColumns();
auto virtuals = getVirtualsPtr();
if (!hasDynamicSubcolumns(columns))
if (!hasDynamicSubcolumnsDeprecated(columns))
return;
const auto & part_columns = part->getColumns();
@ -8754,8 +8754,11 @@ void MergeTreeData::resetObjectColumnsFromActiveParts(const DataPartsLock & /*lo
{
auto metadata_snapshot = getInMemoryMetadataPtr();
const auto & columns = metadata_snapshot->getColumns();
if (!hasDynamicSubcolumns(columns))
if (!hasDynamicSubcolumnsDeprecated(columns))
{
object_columns = ColumnsDescription{};
return;
}
auto range = getDataPartsStateRange(DataPartState::Active);
object_columns = getConcreteObjectColumns(range, columns);
@ -8765,7 +8768,7 @@ void MergeTreeData::updateObjectColumns(const DataPartPtr & part, const DataPart
{
auto metadata_snapshot = getInMemoryMetadataPtr();
const auto & columns = metadata_snapshot->getColumns();
if (!hasDynamicSubcolumns(columns))
if (!hasDynamicSubcolumnsDeprecated(columns))
return;
DB::updateObjectColumns(object_columns, columns, part->getColumns());

View File

@ -50,7 +50,7 @@ StorageSnapshotPtr
StorageFromMergeTreeDataPart::getStorageSnapshot(const StorageMetadataPtr & metadata_snapshot, ContextPtr /*query_context*/) const
{
const auto & storage_columns = metadata_snapshot->getColumns();
if (!hasDynamicSubcolumns(storage_columns))
if (!hasDynamicSubcolumnsDeprecated(storage_columns))
return std::make_shared<StorageSnapshot>(*this, metadata_snapshot);
auto data_parts = storage.getDataPartsVectorForInternalUsage();

View File

@ -686,7 +686,7 @@ std::optional<QueryProcessingStage::Enum> StorageDistributed::getOptimizedQueryP
static bool requiresObjectColumns(const ColumnsDescription & all_columns, ASTPtr query)
{
if (!hasDynamicSubcolumns(all_columns))
if (!hasDynamicSubcolumnsDeprecated(all_columns))
return false;
if (!query)

View File

@ -180,7 +180,7 @@ StorageSnapshotPtr StorageMemory::getStorageSnapshot(const StorageMetadataPtr &
/// rows and bytes counters into the MultiVersion-ed struct, then everything would be consistent.
snapshot_data->rows_approx = total_size_rows.load(std::memory_order_relaxed);
if (!hasDynamicSubcolumns(metadata_snapshot->getColumns()))
if (!hasDynamicSubcolumnsDeprecated(metadata_snapshot->getColumns()))
return std::make_shared<StorageSnapshot>(*this, metadata_snapshot, ColumnsDescription{}, std::move(snapshot_data));
auto object_columns = getConcreteObjectColumns(

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@ -226,6 +226,26 @@ template class TableFunctionObjectStorage<HDFSClusterDefinition, StorageHDFSConf
#endif
template class TableFunctionObjectStorage<LocalDefinition, StorageLocalConfiguration>;
#if USE_AVRO && USE_AWS_S3
template class TableFunctionObjectStorage<IcebergS3ClusterDefinition, StorageS3IcebergConfiguration>;
#endif
#if USE_AVRO && USE_AZURE_BLOB_STORAGE
template class TableFunctionObjectStorage<IcebergAzureClusterDefinition, StorageAzureIcebergConfiguration>;
#endif
#if USE_AVRO && USE_HDFS
template class TableFunctionObjectStorage<IcebergHDFSClusterDefinition, StorageHDFSIcebergConfiguration>;
#endif
#if USE_PARQUET && USE_AWS_S3
template class TableFunctionObjectStorage<DeltaLakeClusterDefinition, StorageS3DeltaLakeConfiguration>;
#endif
#if USE_AWS_S3
template class TableFunctionObjectStorage<HudiClusterDefinition, StorageS3HudiConfiguration>;
#endif
#if USE_AVRO
void registerTableFunctionIceberg(TableFunctionFactory & factory)
{

View File

@ -96,7 +96,7 @@ void registerTableFunctionObjectStorageCluster(TableFunctionFactory & factory)
{
.documentation = {
.description=R"(The table function can be used to read the data stored on HDFS in parallel for many nodes in a specified cluster.)",
.examples{{"HDFSCluster", "SELECT * FROM HDFSCluster(cluster_name, uri, format)", ""}}},
.examples{{"HDFSCluster", "SELECT * FROM HDFSCluster(cluster, uri, format)", ""}}},
.allow_readonly = false
}
);
@ -105,15 +105,77 @@ void registerTableFunctionObjectStorageCluster(TableFunctionFactory & factory)
UNUSED(factory);
}
#if USE_AVRO
void registerTableFunctionIcebergCluster(TableFunctionFactory & factory)
{
UNUSED(factory);
#if USE_AWS_S3
template class TableFunctionObjectStorageCluster<S3ClusterDefinition, StorageS3Configuration>;
factory.registerFunction<TableFunctionIcebergS3Cluster>(
{.documentation
= {.description = R"(The table function can be used to read the Iceberg table stored on S3 object store in parallel for many nodes in a specified cluster.)",
.examples{{"icebergS3Cluster", "SELECT * FROM icebergS3Cluster(cluster, url, [, NOSIGN | access_key_id, secret_access_key, [session_token]], format, [,compression])", ""}},
.categories{"DataLake"}},
.allow_readonly = false});
#endif
#if USE_AZURE_BLOB_STORAGE
template class TableFunctionObjectStorageCluster<AzureClusterDefinition, StorageAzureConfiguration>;
factory.registerFunction<TableFunctionIcebergAzureCluster>(
{.documentation
= {.description = R"(The table function can be used to read the Iceberg table stored on Azure object store in parallel for many nodes in a specified cluster.)",
.examples{{"icebergAzureCluster", "SELECT * FROM icebergAzureCluster(cluster, connection_string|storage_account_url, container_name, blobpath, [account_name, account_key, format, compression])", ""}},
.categories{"DataLake"}},
.allow_readonly = false});
#endif
#if USE_HDFS
template class TableFunctionObjectStorageCluster<HDFSClusterDefinition, StorageHDFSConfiguration>;
factory.registerFunction<TableFunctionIcebergHDFSCluster>(
{.documentation
= {.description = R"(The table function can be used to read the Iceberg table stored on HDFS virtual filesystem in parallel for many nodes in a specified cluster.)",
.examples{{"icebergHDFSCluster", "SELECT * FROM icebergHDFSCluster(cluster, uri, [format], [structure], [compression_method])", ""}},
.categories{"DataLake"}},
.allow_readonly = false});
#endif
}
#endif
#if USE_AWS_S3
#if USE_PARQUET
void registerTableFunctionDeltaLakeCluster(TableFunctionFactory & factory)
{
factory.registerFunction<TableFunctionDeltaLakeCluster>(
{.documentation
= {.description = R"(The table function can be used to read the DeltaLake table stored on object store in parallel for many nodes in a specified cluster.)",
.examples{{"deltaLakeCluster", "SELECT * FROM deltaLakeCluster(cluster, url, access_key_id, secret_access_key)", ""}},
.categories{"DataLake"}},
.allow_readonly = false});
}
#endif
void registerTableFunctionHudiCluster(TableFunctionFactory & factory)
{
factory.registerFunction<TableFunctionHudiCluster>(
{.documentation
= {.description = R"(The table function can be used to read the Hudi table stored on object store in parallel for many nodes in a specified cluster.)",
.examples{{"hudiCluster", "SELECT * FROM hudiCluster(cluster, url, access_key_id, secret_access_key)", ""}},
.categories{"DataLake"}},
.allow_readonly = false});
}
#endif
void registerDataLakeClusterTableFunctions(TableFunctionFactory & factory)
{
UNUSED(factory);
#if USE_AVRO
registerTableFunctionIcebergCluster(factory);
#endif
#if USE_AWS_S3
#if USE_PARQUET
registerTableFunctionDeltaLakeCluster(factory);
#endif
registerTableFunctionHudiCluster(factory);
#endif
}
}

View File

@ -33,6 +33,36 @@ struct HDFSClusterDefinition
static constexpr auto storage_type_name = "HDFSCluster";
};
struct IcebergS3ClusterDefinition
{
static constexpr auto name = "icebergS3Cluster";
static constexpr auto storage_type_name = "IcebergS3Cluster";
};
struct IcebergAzureClusterDefinition
{
static constexpr auto name = "icebergAzureCluster";
static constexpr auto storage_type_name = "IcebergAzureCluster";
};
struct IcebergHDFSClusterDefinition
{
static constexpr auto name = "icebergHDFSCluster";
static constexpr auto storage_type_name = "IcebergHDFSCluster";
};
struct DeltaLakeClusterDefinition
{
static constexpr auto name = "deltaLakeCluster";
static constexpr auto storage_type_name = "DeltaLakeS3Cluster";
};
struct HudiClusterDefinition
{
static constexpr auto name = "hudiCluster";
static constexpr auto storage_type_name = "HudiS3Cluster";
};
/**
* Class implementing s3/hdfs/azureBlobStorageCluster(...) table functions,
* which allow to process many files from S3/HDFS/Azure blob storage on a specific cluster.
@ -79,4 +109,25 @@ using TableFunctionAzureBlobCluster = TableFunctionObjectStorageCluster<AzureClu
#if USE_HDFS
using TableFunctionHDFSCluster = TableFunctionObjectStorageCluster<HDFSClusterDefinition, StorageHDFSConfiguration>;
#endif
#if USE_AVRO && USE_AWS_S3
using TableFunctionIcebergS3Cluster = TableFunctionObjectStorageCluster<IcebergS3ClusterDefinition, StorageS3IcebergConfiguration>;
#endif
#if USE_AVRO && USE_AZURE_BLOB_STORAGE
using TableFunctionIcebergAzureCluster = TableFunctionObjectStorageCluster<IcebergAzureClusterDefinition, StorageAzureIcebergConfiguration>;
#endif
#if USE_AVRO && USE_HDFS
using TableFunctionIcebergHDFSCluster = TableFunctionObjectStorageCluster<IcebergHDFSClusterDefinition, StorageHDFSIcebergConfiguration>;
#endif
#if USE_AWS_S3 && USE_PARQUET
using TableFunctionDeltaLakeCluster = TableFunctionObjectStorageCluster<DeltaLakeClusterDefinition, StorageS3DeltaLakeConfiguration>;
#endif
#if USE_AWS_S3
using TableFunctionHudiCluster = TableFunctionObjectStorageCluster<HudiClusterDefinition, StorageS3HudiConfiguration>;
#endif
}

View File

@ -66,6 +66,7 @@ void registerTableFunctions(bool use_legacy_mongodb_integration [[maybe_unused]]
registerTableFunctionObjectStorage(factory);
registerTableFunctionObjectStorageCluster(factory);
registerDataLakeTableFunctions(factory);
registerDataLakeClusterTableFunctions(factory);
}
}

View File

@ -70,6 +70,7 @@ void registerTableFunctionExplain(TableFunctionFactory & factory);
void registerTableFunctionObjectStorage(TableFunctionFactory & factory);
void registerTableFunctionObjectStorageCluster(TableFunctionFactory & factory);
void registerDataLakeTableFunctions(TableFunctionFactory & factory);
void registerDataLakeClusterTableFunctions(TableFunctionFactory & factory);
void registerTableFunctionTimeSeries(TableFunctionFactory & factory);

View File

@ -0,0 +1,20 @@
<clickhouse>
<remote_servers>
<cluster_simple>
<shard>
<replica>
<host>node1</host>
<port>9000</port>
</replica>
<replica>
<host>node2</host>
<port>9000</port>
</replica>
<replica>
<host>node3</host>
<port>9000</port>
</replica>
</shard>
</cluster_simple>
</remote_servers>
</clickhouse>

View File

@ -0,0 +1,6 @@
<clickhouse>
<query_log>
<database>system</database>
<table>query_log</table>
</query_log>
</clickhouse>

View File

@ -73,14 +73,38 @@ def started_cluster():
cluster.add_instance(
"node1",
main_configs=[
"configs/config.d/query_log.xml",
"configs/config.d/cluster.xml",
"configs/config.d/named_collections.xml",
"configs/config.d/filesystem_caches.xml",
],
user_configs=["configs/users.d/users.xml"],
with_minio=True,
with_azurite=True,
stay_alive=True,
with_hdfs=with_hdfs,
stay_alive=True,
)
cluster.add_instance(
"node2",
main_configs=[
"configs/config.d/query_log.xml",
"configs/config.d/cluster.xml",
"configs/config.d/named_collections.xml",
"configs/config.d/filesystem_caches.xml",
],
user_configs=["configs/users.d/users.xml"],
stay_alive=True,
)
cluster.add_instance(
"node3",
main_configs=[
"configs/config.d/query_log.xml",
"configs/config.d/cluster.xml",
"configs/config.d/named_collections.xml",
"configs/config.d/filesystem_caches.xml",
],
user_configs=["configs/users.d/users.xml"],
stay_alive=True,
)
logging.info("Starting cluster...")
@ -182,6 +206,7 @@ def get_creation_expression(
cluster,
format="Parquet",
table_function=False,
run_on_cluster=False,
**kwargs,
):
if storage_type == "s3":
@ -189,35 +214,56 @@ def get_creation_expression(
bucket = kwargs["bucket"]
else:
bucket = cluster.minio_bucket
print(bucket)
if table_function:
return f"icebergS3(s3, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'http://minio1:9001/{bucket}/')"
if run_on_cluster:
assert table_function
return f"icebergS3Cluster('cluster_simple', s3, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'http://minio1:9001/{bucket}/')"
else:
return f"""
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=IcebergS3(s3, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'http://minio1:9001/{bucket}/')"""
if table_function:
return f"icebergS3(s3, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'http://minio1:9001/{bucket}/')"
else:
return f"""
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=IcebergS3(s3, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'http://minio1:9001/{bucket}/')"""
elif storage_type == "azure":
if table_function:
if run_on_cluster:
assert table_function
return f"""
icebergAzure(azure, container = '{cluster.azure_container_name}', storage_account_url = '{cluster.env_variables["AZURITE_STORAGE_ACCOUNT_URL"]}', blob_path = '/iceberg_data/default/{table_name}/', format={format})
icebergAzureCluster('cluster_simple', azure, container = '{cluster.azure_container_name}', storage_account_url = '{cluster.env_variables["AZURITE_STORAGE_ACCOUNT_URL"]}', blob_path = '/iceberg_data/default/{table_name}/', format={format})
"""
else:
return f"""
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=IcebergAzure(azure, container = {cluster.azure_container_name}, storage_account_url = '{cluster.env_variables["AZURITE_STORAGE_ACCOUNT_URL"]}', blob_path = '/iceberg_data/default/{table_name}/', format={format})"""
if table_function:
return f"""
icebergAzure(azure, container = '{cluster.azure_container_name}', storage_account_url = '{cluster.env_variables["AZURITE_STORAGE_ACCOUNT_URL"]}', blob_path = '/iceberg_data/default/{table_name}/', format={format})
"""
else:
return f"""
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=IcebergAzure(azure, container = {cluster.azure_container_name}, storage_account_url = '{cluster.env_variables["AZURITE_STORAGE_ACCOUNT_URL"]}', blob_path = '/iceberg_data/default/{table_name}/', format={format})"""
elif storage_type == "hdfs":
if table_function:
if run_on_cluster:
assert table_function
return f"""
icebergHDFS(hdfs, filename= 'iceberg_data/default/{table_name}/', format={format}, url = 'hdfs://hdfs1:9000/')
icebergHDFSCluster('cluster_simple', hdfs, filename= 'iceberg_data/default/{table_name}/', format={format}, url = 'hdfs://hdfs1:9000/')
"""
else:
return f"""
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=IcebergHDFS(hdfs, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'hdfs://hdfs1:9000/');"""
if table_function:
return f"""
icebergHDFS(hdfs, filename= 'iceberg_data/default/{table_name}/', format={format}, url = 'hdfs://hdfs1:9000/')
"""
else:
return f"""
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=IcebergHDFS(hdfs, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'hdfs://hdfs1:9000/');"""
elif storage_type == "local":
assert not run_on_cluster
if table_function:
return f"""
icebergLocal(local, path = '/iceberg_data/default/{table_name}/', format={format})
@ -227,6 +273,7 @@ def get_creation_expression(
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=IcebergLocal(local, path = '/iceberg_data/default/{table_name}/', format={format});"""
else:
raise Exception(f"Unknown iceberg storage type: {storage_type}")
@ -492,6 +539,108 @@ def test_types(started_cluster, format_version, storage_type):
)
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs"])
def test_cluster_table_function(started_cluster, format_version, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_iceberg_cluster_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
def add_df(mode):
write_iceberg_from_df(
spark,
generate_data(spark, 0, 100),
TABLE_NAME,
mode=mode,
format_version=format_version,
)
files = default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
logging.info(f"Adding another dataframe. result files: {files}")
return files
files = add_df(mode="overwrite")
for i in range(1, len(started_cluster.instances)):
files = add_df(mode="append")
logging.info(f"Setup complete. files: {files}")
assert len(files) == 5 + 4 * (len(started_cluster.instances) - 1)
clusters = instance.query(f"SELECT * FROM system.clusters")
logging.info(f"Clusters setup: {clusters}")
# Regular Query only node1
table_function_expr = get_creation_expression(
storage_type, TABLE_NAME, started_cluster, table_function=True
)
select_regular = (
instance.query(f"SELECT * FROM {table_function_expr}").strip().split()
)
# Cluster Query with node1 as coordinator
table_function_expr_cluster = get_creation_expression(
storage_type,
TABLE_NAME,
started_cluster,
table_function=True,
run_on_cluster=True,
)
select_cluster = (
instance.query(f"SELECT * FROM {table_function_expr_cluster}").strip().split()
)
# Simple size check
assert len(select_regular) == 600
assert len(select_cluster) == 600
# Actual check
assert select_cluster == select_regular
# Check query_log
for replica in started_cluster.instances.values():
replica.query("SYSTEM FLUSH LOGS")
for node_name, replica in started_cluster.instances.items():
cluster_secondary_queries = (
replica.query(
f"""
SELECT query, type, is_initial_query, read_rows, read_bytes FROM system.query_log
WHERE
type = 'QueryStart' AND
positionCaseInsensitive(query, '{storage_type}Cluster') != 0 AND
position(query, '{TABLE_NAME}') != 0 AND
position(query, 'system.query_log') = 0 AND
NOT is_initial_query
"""
)
.strip()
.split("\n")
)
logging.info(
f"[{node_name}] cluster_secondary_queries: {cluster_secondary_queries}"
)
assert len(cluster_secondary_queries) == 1
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
def test_delete_files(started_cluster, format_version, storage_type):

View File

@ -3,13 +3,7 @@
SET allow_experimental_object_type=1;
DROP TABLE IF EXISTS t_to;
DROP TABLE IF EXISTS t_from;
CREATE TABLE t_to (id UInt64, value Nullable(String)) ENGINE MergeTree() ORDER BY id;
CREATE TABLE t_from (id UInt64, value Object('json')) ENGINE MergeTree() ORDER BY id;
ALTER TABLE t_to MODIFY COLUMN value Object('json'); -- { serverError BAD_ARGUMENTS }
ALTER TABLE t_from MODIFY COLUMN value Nullable(String); -- { serverError BAD_ARGUMENTS }
DROP TABLE t_to;
DROP TABLE t_from;

View File

@ -1,3 +1,6 @@
SELECT arrayWithConstant(96142475, ['qMUF']); -- { serverError TOO_LARGE_ARRAY_SIZE }
SELECT arrayWithConstant(100000000, materialize([[[[[[[[[['Hello, world!']]]]]]]]]])); -- { serverError TOO_LARGE_ARRAY_SIZE }
SELECT length(arrayWithConstant(10000000, materialize([[[[[[[[[['Hello world']]]]]]]]]])));
CREATE TEMPORARY TABLE args (value Array(Int)) ENGINE=Memory AS SELECT [1, 1, 1, 1] as value FROM numbers(1, 100);
SELECT length(arrayWithConstant(1000000, value)) FROM args FORMAT NULL;

View File

@ -1,20 +1,20 @@
Map to JSON
{"a":"0","b":"1970-01-01","c":[],"d":[{"e":"0"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"1","b":"1970-01-02","c":["0"],"d":[{"e":"1"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"2","b":"1970-01-03","c":["0","1"],"d":[{"e":"2"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"3","b":"1970-01-04","c":["0","1","2"],"d":[{"e":"3"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"4","b":"1970-01-05","c":["0","1","2","3"],"d":[{"e":"4"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"1","b":"1970-01-02","c":["0"],"d":[{"e":"1"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(Int64))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"2","b":"1970-01-03","c":["0","1"],"d":[{"e":"2"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(Int64))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"3","b":"1970-01-04","c":["0","1","2"],"d":[{"e":"3"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(Int64))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"4","b":"1970-01-05","c":["0","1","2","3"],"d":[{"e":"4"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(Int64))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a0":"0","b0":"1970-01-01","c0":[],"d0":[{"e0":"0"}]} {'a0':'Int64','b0':'Date','c0':'Array(Nullable(String))','d0':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a1":"1","b1":"1970-01-02","c1":["0"],"d1":[{"e1":"1"}]} {'a1':'Int64','b1':'Date','c1':'Array(Nullable(String))','d1':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a2":"2","b2":"1970-01-03","c2":["0","1"],"d2":[{"e2":"2"}]} {'a2':'Int64','b2':'Date','c2':'Array(Nullable(String))','d2':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a0":"3","b0":"1970-01-04","c0":["0","1","2"],"d0":[{"e0":"3"}]} {'a0':'Int64','b0':'Date','c0':'Array(Nullable(String))','d0':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a1":"4","b1":"1970-01-05","c1":["0","1","2","3"],"d1":[{"e1":"4"}]} {'a1':'Int64','b1':'Date','c1':'Array(Nullable(String))','d1':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a1":"1","b1":"1970-01-02","c1":["0"],"d1":[{"e1":"1"}]} {'a1':'Int64','b1':'Date','c1':'Array(Nullable(Int64))','d1':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a2":"2","b2":"1970-01-03","c2":["0","1"],"d2":[{"e2":"2"}]} {'a2':'Int64','b2':'Date','c2':'Array(Nullable(Int64))','d2':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a0":"3","b0":"1970-01-04","c0":["0","1","2"],"d0":[{"e0":"3"}]} {'a0':'Int64','b0':'Date','c0':'Array(Nullable(Int64))','d0':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a1":"4","b1":"1970-01-05","c1":["0","1","2","3"],"d1":[{"e1":"4"}]} {'a1':'Int64','b1':'Date','c1':'Array(Nullable(Int64))','d1':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
Tuple to JSON
{"a":"0","b":"1970-01-01","c":[],"d":[{"e":"0"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"1","b":"1970-01-02","c":["0"],"d":[{"e":"1"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"2","b":"1970-01-03","c":["0","1"],"d":[{"e":"2"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"3","b":"1970-01-04","c":["0","1","2"],"d":[{"e":"3"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"4","b":"1970-01-05","c":["0","1","2","3"],"d":[{"e":"4"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"1","b":"1970-01-02","c":["0"],"d":[{"e":"1"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(Int64))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"2","b":"1970-01-03","c":["0","1"],"d":[{"e":"2"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(Int64))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"3","b":"1970-01-04","c":["0","1","2"],"d":[{"e":"3"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(Int64))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
{"a":"4","b":"1970-01-05","c":["0","1","2","3"],"d":[{"e":"4"}]} {'a':'Int64','b':'Date','c':'Array(Nullable(Int64))','d':'Array(JSON(max_dynamic_types=16, max_dynamic_paths=256))'}
Object to JSON
{"a":"0","b":"1970-01-01","c":[],"d":{"e":["0"]}} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d.e':'Array(Nullable(Int64))'}
{"a":"1","b":"1970-01-02","c":["0"],"d":{"e":["1"]}} {'a':'Int64','b':'Date','c':'Array(Nullable(String))','d.e':'Array(Nullable(Int64))'}

View File

@ -0,0 +1,132 @@
{'a0':['Int64'],'a1':['Int64'],'a10':['Int64'],'a11':['Int64'],'a12':['Int64'],'a13':['Int64'],'a14':['Int64'],'a15':['Int64'],'a16':['Int64'],'a17':['Int64'],'a18':['Int64'],'a19':['Int64'],'a2':['Int64'],'a20':['Int64'],'a21':['Int64'],'a22':['Int64'],'a23':['Int64'],'a24':['Int64'],'a25':['Int64'],'a26':['Int64'],'a27':['Int64'],'a28':['Int64'],'a29':['Int64'],'a3':['Int64'],'a30':['Int64'],'a31':['Int64'],'a32':['Int64'],'a33':['Int64'],'a34':['Int64'],'a35':['Int64'],'a36':['Int64'],'a37':['Int64'],'a38':['Int64'],'a39':['Int64'],'a4':['Int64'],'a40':['Int64'],'a41':['Int64'],'a42':['Int64'],'a43':['Int64'],'a44':['Int64'],'a45':['Int64'],'a46':['Int64'],'a47':['Int64'],'a48':['Int64'],'a49':['Int64'],'a5':['Int64'],'a50':['Int64'],'a51':['Int64'],'a52':['Int64'],'a53':['Int64'],'a54':['Int64'],'a55':['Int64'],'a56':['Int64'],'a57':['Int64'],'a58':['Int64'],'a59':['Int64'],'a6':['Int64'],'a60':['Int64'],'a61':['Int64'],'a62':['Int64'],'a63':['Int64'],'a64':['Int64'],'a65':['Int64'],'a66':['Int64'],'a67':['Int64'],'a68':['Int64'],'a69':['Int64'],'a7':['Int64'],'a70':['Int64'],'a71':['Int64'],'a72':['Int64'],'a73':['Int64'],'a74':['Int64'],'a75':['Int64'],'a76':['Int64'],'a77':['Int64'],'a78':['Int64'],'a79':['Int64'],'a8':['Int64'],'a80':['Int64'],'a81':['Int64'],'a82':['Int64'],'a83':['Int64'],'a84':['Int64'],'a85':['Int64'],'a86':['Int64'],'a87':['Int64'],'a88':['Int64'],'a89':['Int64'],'a9':['Int64'],'a90':['Int64'],'a91':['Int64'],'a92':['Int64'],'a93':['Int64'],'a94':['Int64'],'a95':['Int64'],'a96':['Int64'],'a97':['Int64'],'a98':['Int64'],'a99':['Int64']}
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{'a0':['Int64'],'a1':['Int64'],'a10':['Int64'],'a11':['Int64'],'a12':['Int64'],'a13':['Int64'],'a14':['Int64'],'a15':['Int64'],'a16':['Int64'],'a17':['Int64'],'a18':['Int64'],'a19':['Int64'],'a2':['Int64'],'a20':['Int64'],'a21':['Int64'],'a22':['Int64'],'a23':['Int64'],'a24':['Int64'],'a25':['Int64'],'a26':['Int64'],'a27':['Int64'],'a28':['Int64'],'a29':['Int64'],'a3':['Int64'],'a30':['Int64'],'a31':['Int64'],'a32':['Int64'],'a33':['Int64'],'a34':['Int64'],'a35':['Int64'],'a36':['Int64'],'a37':['Int64'],'a38':['Int64'],'a39':['Int64'],'a4':['Int64'],'a40':['Int64'],'a41':['Int64'],'a42':['Int64'],'a43':['Int64'],'a44':['Int64'],'a45':['Int64'],'a46':['Int64'],'a47':['Int64'],'a48':['Int64'],'a49':['Int64'],'a5':['Int64'],'a50':['Int64'],'a51':['Int64'],'a52':['Int64'],'a53':['Int64'],'a54':['Int64'],'a55':['Int64'],'a56':['Int64'],'a57':['Int64'],'a58':['Int64'],'a59':['Int64'],'a6':['Int64'],'a60':['Int64'],'a61':['Int64'],'a62':['Int64'],'a63':['Int64'],'a64':['Int64'],'a65':['Int64'],'a66':['Int64'],'a67':['Int64'],'a68':['Int64'],'a69':['Int64'],'a7':['Int64'],'a70':['Int64'],'a71':['Int64'],'a72':['Int64'],'a73':['Int64'],'a74':['Int64'],'a75':['Int64'],'a76':['Int64'],'a77':['Int64'],'a78':['Int64'],'a79':['Int64'],'a8':['Int64'],'a80':['Int64'],'a81':['Int64'],'a82':['Int64'],'a83':['Int64'],'a84':['Int64'],'a85':['Int64'],'a86':['Int64'],'a87':['Int64'],'a88':['Int64'],'a89':['Int64'],'a9':['Int64'],'a90':['Int64'],'a91':['Int64'],'a92':['Int64'],'a93':['Int64'],'a94':['Int64'],'a95':['Int64'],'a96':['Int64'],'a97':['Int64'],'a98':['Int64'],'a99':['Int64']}
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{'a0':['Int64'],'a1':['Int64'],'a10':['Int64'],'a11':['Int64'],'a12':['Int64'],'a13':['Int64'],'a14':['Int64'],'a15':['Int64'],'a16':['Int64'],'a17':['Int64'],'a18':['Int64'],'a19':['Int64'],'a2':['Int64'],'a20':['Int64'],'a21':['Int64'],'a22':['Int64'],'a23':['Int64'],'a24':['Int64'],'a25':['Int64'],'a26':['Int64'],'a27':['Int64'],'a28':['Int64'],'a29':['Int64'],'a3':['Int64'],'a30':['Int64'],'a31':['Int64'],'a32':['Int64'],'a33':['Int64'],'a34':['Int64'],'a35':['Int64'],'a36':['Int64'],'a37':['Int64'],'a38':['Int64'],'a39':['Int64'],'a4':['Int64'],'a40':['Int64'],'a41':['Int64'],'a42':['Int64'],'a43':['Int64'],'a44':['Int64'],'a45':['Int64'],'a46':['Int64'],'a47':['Int64'],'a48':['Int64'],'a49':['Int64'],'a5':['Int64'],'a50':['Int64'],'a51':['Int64'],'a52':['Int64'],'a53':['Int64'],'a54':['Int64'],'a55':['Int64'],'a56':['Int64'],'a57':['Int64'],'a58':['Int64'],'a59':['Int64'],'a6':['Int64'],'a60':['Int64'],'a61':['Int64'],'a62':['Int64'],'a63':['Int64'],'a64':['Int64'],'a65':['Int64'],'a66':['Int64'],'a67':['Int64'],'a68':['Int64'],'a69':['Int64'],'a7':['Int64'],'a70':['Int64'],'a71':['Int64'],'a72':['Int64'],'a73':['Int64'],'a74':['Int64'],'a75':['Int64'],'a76':['Int64'],'a77':['Int64'],'a78':['Int64'],'a79':['Int64'],'a8':['Int64'],'a80':['Int64'],'a81':['Int64'],'a82':['Int64'],'a83':['Int64'],'a84':['Int64'],'a85':['Int64'],'a86':['Int64'],'a87':['Int64'],'a88':['Int64'],'a89':['Int64'],'a9':['Int64'],'a90':['Int64'],'a91':['Int64'],'a92':['Int64'],'a93':['Int64'],'a94':['Int64'],'a95':['Int64'],'a96':['Int64'],'a97':['Int64'],'a98':['Int64'],'a99':['Int64']}
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{'a0':['Int64'],'a1':['Int64'],'a10':['Int64'],'a11':['Int64'],'a12':['Int64'],'a13':['Int64'],'a14':['Int64'],'a15':['Int64'],'a16':['Int64'],'a17':['Int64'],'a18':['Int64'],'a19':['Int64'],'a2':['Int64'],'a20':['Int64'],'a21':['Int64'],'a22':['Int64'],'a23':['Int64'],'a24':['Int64'],'a25':['Int64'],'a26':['Int64'],'a27':['Int64'],'a28':['Int64'],'a29':['Int64'],'a3':['Int64'],'a30':['Int64'],'a31':['Int64'],'a32':['Int64'],'a33':['Int64'],'a34':['Int64'],'a35':['Int64'],'a36':['Int64'],'a37':['Int64'],'a38':['Int64'],'a39':['Int64'],'a4':['Int64'],'a40':['Int64'],'a41':['Int64'],'a42':['Int64'],'a43':['Int64'],'a44':['Int64'],'a45':['Int64'],'a46':['Int64'],'a47':['Int64'],'a48':['Int64'],'a49':['Int64'],'a5':['Int64'],'a50':['Int64'],'a51':['Int64'],'a52':['Int64'],'a53':['Int64'],'a54':['Int64'],'a55':['Int64'],'a56':['Int64'],'a57':['Int64'],'a58':['Int64'],'a59':['Int64'],'a6':['Int64'],'a60':['Int64'],'a61':['Int64'],'a62':['Int64'],'a63':['Int64'],'a64':['Int64'],'a65':['Int64'],'a66':['Int64'],'a67':['Int64'],'a68':['Int64'],'a69':['Int64'],'a7':['Int64'],'a70':['Int64'],'a71':['Int64'],'a72':['Int64'],'a73':['Int64'],'a74':['Int64'],'a75':['Int64'],'a76':['Int64'],'a77':['Int64'],'a78':['Int64'],'a79':['Int64'],'a8':['Int64'],'a80':['Int64'],'a81':['Int64'],'a82':['Int64'],'a83':['Int64'],'a84':['Int64'],'a85':['Int64'],'a86':['Int64'],'a87':['Int64'],'a88':['Int64'],'a89':['Int64'],'a9':['Int64'],'a90':['Int64'],'a91':['Int64'],'a92':['Int64'],'a93':['Int64'],'a94':['Int64'],'a95':['Int64'],'a96':['Int64'],'a97':['Int64'],'a98':['Int64'],'a99':['Int64']}
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{'a0':['Int64'],'a1':['Int64'],'a10':['Int64'],'a11':['Int64'],'a12':['Int64'],'a13':['Int64'],'a14':['Int64'],'a15':['Int64'],'a16':['Int64'],'a17':['Int64'],'a18':['Int64'],'a19':['Int64'],'a2':['Int64'],'a20':['Int64'],'a21':['Int64'],'a22':['Int64'],'a23':['Int64'],'a24':['Int64'],'a25':['Int64'],'a26':['Int64'],'a27':['Int64'],'a28':['Int64'],'a29':['Int64'],'a3':['Int64'],'a30':['Int64'],'a31':['Int64'],'a32':['Int64'],'a33':['Int64'],'a34':['Int64'],'a35':['Int64'],'a36':['Int64'],'a37':['Int64'],'a38':['Int64'],'a39':['Int64'],'a4':['Int64'],'a40':['Int64'],'a41':['Int64'],'a42':['Int64'],'a43':['Int64'],'a44':['Int64'],'a45':['Int64'],'a46':['Int64'],'a47':['Int64'],'a48':['Int64'],'a49':['Int64'],'a5':['Int64'],'a50':['Int64'],'a51':['Int64'],'a52':['Int64'],'a53':['Int64'],'a54':['Int64'],'a55':['Int64'],'a56':['Int64'],'a57':['Int64'],'a58':['Int64'],'a59':['Int64'],'a6':['Int64'],'a60':['Int64'],'a61':['Int64'],'a62':['Int64'],'a63':['Int64'],'a64':['Int64'],'a65':['Int64'],'a66':['Int64'],'a67':['Int64'],'a68':['Int64'],'a69':['Int64'],'a7':['Int64'],'a70':['Int64'],'a71':['Int64'],'a72':['Int64'],'a73':['Int64'],'a74':['Int64'],'a75':['Int64'],'a76':['Int64'],'a77':['Int64'],'a78':['Int64'],'a79':['Int64'],'a8':['Int64'],'a80':['Int64'],'a81':['Int64'],'a82':['Int64'],'a83':['Int64'],'a84':['Int64'],'a85':['Int64'],'a86':['Int64'],'a87':['Int64'],'a88':['Int64'],'a89':['Int64'],'a9':['Int64'],'a90':['Int64'],'a91':['Int64'],'a92':['Int64'],'a93':['Int64'],'a94':['Int64'],'a95':['Int64'],'a96':['Int64'],'a97':['Int64'],'a98':['Int64'],'a99':['Int64']}
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{'a0':['Int64'],'a1':['Int64'],'a10':['Int64'],'a11':['Int64'],'a12':['Int64'],'a13':['Int64'],'a14':['Int64'],'a15':['Int64'],'a16':['Int64'],'a17':['Int64'],'a18':['Int64'],'a19':['Int64'],'a2':['Int64'],'a20':['Int64'],'a21':['Int64'],'a22':['Int64'],'a23':['Int64'],'a24':['Int64'],'a25':['Int64'],'a26':['Int64'],'a27':['Int64'],'a28':['Int64'],'a29':['Int64'],'a3':['Int64'],'a30':['Int64'],'a31':['Int64'],'a32':['Int64'],'a33':['Int64'],'a34':['Int64'],'a35':['Int64'],'a36':['Int64'],'a37':['Int64'],'a38':['Int64'],'a39':['Int64'],'a4':['Int64'],'a40':['Int64'],'a41':['Int64'],'a42':['Int64'],'a43':['Int64'],'a44':['Int64'],'a45':['Int64'],'a46':['Int64'],'a47':['Int64'],'a48':['Int64'],'a49':['Int64'],'a5':['Int64'],'a50':['Int64'],'a51':['Int64'],'a52':['Int64'],'a53':['Int64'],'a54':['Int64'],'a55':['Int64'],'a56':['Int64'],'a57':['Int64'],'a58':['Int64'],'a59':['Int64'],'a6':['Int64'],'a60':['Int64'],'a61':['Int64'],'a62':['Int64'],'a63':['Int64'],'a64':['Int64'],'a65':['Int64'],'a66':['Int64'],'a67':['Int64'],'a68':['Int64'],'a69':['Int64'],'a7':['Int64'],'a70':['Int64'],'a71':['Int64'],'a72':['Int64'],'a73':['Int64'],'a74':['Int64'],'a75':['Int64'],'a76':['Int64'],'a77':['Int64'],'a78':['Int64'],'a79':['Int64'],'a8':['Int64'],'a80':['Int64'],'a81':['Int64'],'a82':['Int64'],'a83':['Int64'],'a84':['Int64'],'a85':['Int64'],'a86':['Int64'],'a87':['Int64'],'a88':['Int64'],'a89':['Int64'],'a9':['Int64'],'a90':['Int64'],'a91':['Int64'],'a92':['Int64'],'a93':['Int64'],'a94':['Int64'],'a95':['Int64'],'a96':['Int64'],'a97':['Int64'],'a98':['Int64'],'a99':['Int64']}
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@ -0,0 +1,56 @@
set allow_experimental_object_type = 1;
set allow_experimental_json_type = 1;
set max_block_size = 100;
set max_insert_block_size = 100;
set min_insert_block_size_rows = 100;
set output_format_json_quote_64bit_integers = 0;
drop table if exists test;
create table test (json Object('json')) engine=Memory;
insert into test select toJSONString(map('a' || number % 100, number)) from numbers(1000);
alter table test modify column json JSON;
select distinctJSONPathsAndTypes(json) from test;
select distinct json.a0 from test order by json.a0.:Int64;
select distinct json.a99 from test order by json.a99.:Int64;
drop table test;
create table test (json Object('json')) engine=Memory;
insert into test select toJSONString(map('a' || number % 100, number)) from numbers(1000);
alter table test modify column json JSON(max_dynamic_paths=10);
select distinctJSONPathsAndTypes(json) from test;
select distinct json.a0 from test order by json.a0.:Int64;
select distinct json.a99 from test order by json.a99.:Int64;
drop table test;
create table test (json Object('json')) engine=MergeTree order by tuple() settings min_rows_for_wide_part=10000000, min_bytes_for_wide_part=100000000;
insert into test select toJSONString(map('a' || number % 100, number)) from numbers(1000);
alter table test modify column json JSON;
select distinctJSONPathsAndTypes(json) from test;
select distinct json.a0 from test order by json.a0.:Int64;
select distinct json.a99 from test order by json.a99.:Int64;
drop table test;
create table test (json Object('json')) engine=MergeTree order by tuple() settings min_rows_for_wide_part=10000000, min_bytes_for_wide_part=100000000;
insert into test select toJSONString(map('a' || number % 100, number)) from numbers(1000);
alter table test modify column json JSON(max_dynamic_paths=10);
select distinctJSONPathsAndTypes(json) from test;
select distinct json.a0 from test order by json.a0.:Int64;
select distinct json.a99 from test order by json.a99.:Int64;
drop table test;
create table test (json Object('json')) engine=MergeTree order by tuple() settings min_rows_for_wide_part=1, min_bytes_for_wide_part=1;
insert into test select toJSONString(map('a' || number % 100, number)) from numbers(1000);
alter table test modify column json JSON();
select distinctJSONPathsAndTypes(json) from test;
select distinct json.a0 from test order by json.a0.:Int64;
select distinct json.a99 from test order by json.a99.:Int64;
drop table test;
create table test (json Object('json')) engine=MergeTree order by tuple() settings min_rows_for_wide_part=1, min_bytes_for_wide_part=1;
insert into test select toJSONString(map('a' || number % 100, number)) from numbers(1000);
alter table test modify column json JSON(max_dynamic_paths=10);
select distinctJSONPathsAndTypes(json) from test;
select distinct json.a0 from test order by json.a0.:Int64;
select distinct json.a99 from test order by json.a99.:Int64;
drop table test;

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@ -0,0 +1,12 @@
5000
leonardomso/33-js-concepts 3
ytdl-org/youtube-dl 3
Bogdanp/neko 2
bminossi/AllVideoPocsFromHackerOne 2
disclose/diodata 2
Commit 182
chipeo345 119
phanwi346 114
Nicholas Piggin 95
direwolf-github 49
2

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@ -0,0 +1,29 @@
#!/usr/bin/env bash
# Tags: no-fasttest, long
CUR_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
# shellcheck source=../shell_config.sh
. "$CUR_DIR"/../shell_config.sh
${CLICKHOUSE_CLIENT} -q "DROP TABLE IF EXISTS ghdata"
${CLICKHOUSE_CLIENT} -q "CREATE TABLE ghdata (data Object('json')) ENGINE = MergeTree ORDER BY tuple() SETTINGS index_granularity = 8192, index_granularity_bytes = '10Mi'" --allow_experimental_object_type
cat $CUR_DIR/data_json/ghdata_sample.json | ${CLICKHOUSE_CLIENT} \
--max_memory_usage 10G --query "INSERT INTO ghdata FORMAT JSONAsObject"
${CLICKHOUSE_CLIENT} -q "ALTER TABLE ghdata MODIFY column data JSON SETTINGS mutations_sync=1" --allow_experimental_json_type 1
${CLICKHOUSE_CLIENT} -q "SELECT count() FROM ghdata WHERE NOT ignore(*)"
${CLICKHOUSE_CLIENT} -q \
"SELECT data.repo.name, count() AS stars FROM ghdata \
WHERE data.type = 'WatchEvent' GROUP BY data.repo.name ORDER BY stars DESC, data.repo.name LIMIT 5" --allow_suspicious_types_in_group_by=1 --allow_suspicious_types_in_order_by=1
${CLICKHOUSE_CLIENT} --enable_analyzer=1 -q \
"SELECT data.payload.commits[].author.name AS name, count() AS c FROM ghdata \
ARRAY JOIN data.payload.commits[].author.name \
GROUP BY name ORDER BY c DESC, name LIMIT 5" --allow_suspicious_types_in_group_by=1 --allow_suspicious_types_in_order_by=1
${CLICKHOUSE_CLIENT} -q "SELECT max(data.payload.pull_request.assignees[].size0) FROM ghdata"
${CLICKHOUSE_CLIENT} -q "DROP TABLE IF EXISTS ghdata"

View File

@ -0,0 +1,12 @@
5000
leonardomso/33-js-concepts 3
ytdl-org/youtube-dl 3
Bogdanp/neko 2
bminossi/AllVideoPocsFromHackerOne 2
disclose/diodata 2
Commit 182
chipeo345 119
phanwi346 114
Nicholas Piggin 95
direwolf-github 49
2

View File

@ -0,0 +1,29 @@
#!/usr/bin/env bash
# Tags: no-fasttest, no-msan, no-tsan, no-asan, long
CUR_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
# shellcheck source=../shell_config.sh
. "$CUR_DIR"/../shell_config.sh
${CLICKHOUSE_CLIENT} -q "DROP TABLE IF EXISTS ghdata"
${CLICKHOUSE_CLIENT} -q "CREATE TABLE ghdata (data Object('json')) ENGINE = MergeTree ORDER BY tuple() SETTINGS index_granularity = 8192, index_granularity_bytes = '10Mi'" --allow_experimental_object_type=1
cat $CUR_DIR/data_json/ghdata_sample.json | ${CLICKHOUSE_CLIENT} \
--max_memory_usage 10G --query "INSERT INTO ghdata FORMAT JSONAsObject"
${CLICKHOUSE_CLIENT} -q "ALTER TABLE ghdata MODIFY column data JSON(max_dynamic_paths=8) SETTINGS mutations_sync=1" --allow_experimental_json_type 1
${CLICKHOUSE_CLIENT} -q "SELECT count() FROM ghdata WHERE NOT ignore(*)"
${CLICKHOUSE_CLIENT} -q \
"SELECT data.repo.name, count() AS stars FROM ghdata \
WHERE data.type = 'WatchEvent' GROUP BY data.repo.name ORDER BY stars DESC, data.repo.name LIMIT 5" --allow_suspicious_types_in_group_by=1 --allow_suspicious_types_in_order_by=1
${CLICKHOUSE_CLIENT} --enable_analyzer=1 -q \
"SELECT data.payload.commits[].author.name AS name, count() AS c FROM ghdata \
ARRAY JOIN data.payload.commits[].author.name \
GROUP BY name ORDER BY c DESC, name LIMIT 5" --allow_suspicious_types_in_group_by=1 --allow_suspicious_types_in_order_by=1
${CLICKHOUSE_CLIENT} -q "SELECT max(data.payload.pull_request.assignees[].size0) FROM ghdata"
${CLICKHOUSE_CLIENT} -q "DROP TABLE IF EXISTS ghdata"

View File

@ -244,7 +244,10 @@ Deduplication
DefaultTableEngine
DelayedInserts
DeliveryTag
Deltalake
DeltaLake
deltalakeCluster
deltaLakeCluster
Denormalize
DestroyAggregatesThreads
DestroyAggregatesThreadsActive
@ -377,10 +380,15 @@ Homebrew's
HorizontalDivide
Hostname
HouseOps
hudi
Hudi
hudiCluster
HudiCluster
HyperLogLog
Hypot
IANA
icebergCluster
IcebergCluster
IDE
IDEs
IDNA