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Partitioning policy

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Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer

The partitioning policy defines if and how extents (data shards) should be partitioned for a specific table or a materialized view.

The policy triggers an additional background process that takes place after the creation of extents, following data ingestion. This process includes reingesting data from the source extents and producing homogeneous extents, in which all values of the column designated as the partition key reside within a single partition.

The primary objective of the partitioning policy is to enhance query performance in specific supported scenarios.

Note

By default, when a data partitioning policy isn't defined (is null), extents are partitioned by time of creation (ingestion). In most cases there's no need to set a data partitioning policy.

Supported scenarios

The following are the only scenarios in which setting a data partitioning policy is recommended. In all other scenarios, setting the policy isn't advised.

  • Frequent filters on a medium or high cardinality string or guid column:
    • For example: multitenant solutions, or a metrics table where most or all queries filter on a column of type string or guid, such as the TenantId or the MetricId.
    • Medium cardinality is at least 10,000 distinct values.
    • Set the hash partition key to be the string or guid column, and set the PartitionAssignmentMode property to uniform.
  • Frequent aggregations or joins on a high cardinality string or guid column:
    • For example, IoT information from many different sensors, or academic records of many different students.
    • High cardinality is at least 1,000,000 distinct values, where the distribution of values in the column is approximately even.
    • In this case, set the hash partition key to be the column frequently grouped-by or joined-on, and set the PartitionAssignmentMode property to ByPartition.
  • Out-of-order data ingestion:
    • Data ingested into a table might not be ordered and partitioned into extents (shards) according to a specific datetime column that represents the data creation time and is commonly used to filter data. This could be due to a backfill from heterogeneous source files that include datetime values over a large time span.
    • In this case, set the uniform range datetime partition key to be the datetime column.
    • If you need retention and caching policies to align with the datetime values in the column, instead of aligning with the time of ingestion, set the OverrideCreationTime property to true.

Caution

  • There are no hard-coded limits set on the number of tables with the partitioning policy defined. But, every additional table adds overhead to the background data partitioning process. Setting a policy on more tables will result in more resources being used, and higher cost due to underlying storage transactions. For more information, see capacity.
  • It isn't recommended to set a partitioning policy if the compressed size of data per partition is expected to be less than 1GB.
  • The partitioning process results in residual storage artifacts for all the extents replaced during the partitioning process and during the merge process. Most of the residual storage artifacts are expected to be deleted during the automatic cleanup process. Increasing the value of the MaxPartitionCount property increases the number of residual storage artifacts and can reduce the cleanup performance.
  • Before applying a partitioning policy on a materialized view, review the recommendations for materialized views partitioning policy.

Partition keys

The following kinds of partition keys are supported.

Kind Column Type Partition properties Partition value
Hash string or guid Function, MaxPartitionCount, Seed, PartitionAssignmentMode Function(ColumnName, MaxPartitionCount, Seed)
Uniform range datetime RangeSize, Reference, OverrideCreationTime bin_at(ColumnName, RangeSize, Reference)

Hash partition key

If the policy includes a hash partition key, all homogeneous extents that belong to the same partition will be assigned to the same data node.

Note

The data partitioning operation adds significant processing load. We recommend applying a hash partition key on a table only under the following conditions:

  • If the majority of queries use equality filters (==, in()).
  • The majority of queries aggregate/join on a specific column of type string or guid which is of large-dimension (cardinality of 10M or higher), such as an device_ID, or user_ID.
  • The usage pattern of the partitioned tables is in high concurrency query load, such as in monitoring or dashboarding applications.
  • A hash-modulo function is used to partition the data.
  • Data in homogeneous (partitioned) extents is ordered by the hash partition key.
    • You don't need to include the hash partition key in the row order policy, if one is defined on the table.
  • Queries that use the shuffle strategy, and in which the shuffle key used in join, summarize or make-series is the table's hash partition key, are expected to perform better because the amount of data required to move across nodes is reduced.

Partition properties

Property Description Supported value(s) Recommended value
Function The name of a hash-modulo function to use. XxHash64
MaxPartitionCount The maximum number of partitions to create (the modulo argument to the hash-modulo function) per time period. In the range (1,2048]. Higher values lead to greater overhead of the data partitioning process, and a higher number of extents for each time period. The recommended value is 128. Higher values will significantly increase the overhead of partitioning the data post-ingestion, and the size of metadata - and are therefore not recommended.
Seed Use for randomizing the hash value. A positive integer. 1, which is also the default value.
PartitionAssignmentMode The mode used for assigning partitions to nodes. ByPartition: All homogeneous (partitioned) extents that belong to the same partition are assigned to the same node.
Uniform: An extents' partition values are disregarded. Extents are assigned uniformly to the nodes.		 If queries don't join or aggregate on the hash partition key, use Uniform. Otherwise, use ByPartition.

Hash partition key example

A hash partition key over a string-typed column named tenant_id. It uses the XxHash64 hash function, with MaxPartitionCount set to the recommended value 128, and the default Seed of 1.

{
  "ColumnName": "tenant_id",
  "Kind": "Hash",
  "Properties": {
    "Function": "XxHash64",
    "MaxPartitionCount": 128,
    "Seed": 1,
    "PartitionAssignmentMode": "Uniform"
  }
}

Uniform range datetime partition key

Note

Only apply a uniform range datetime partition key on a datetime-typed column in a table when data ingested into the table is unlikely to be ordered according to this column.

In these cases, you can reshuffle the data between extents so that each extent includes records from a limited time range. This process results in filters on the datetime column being more effective at query time.

The partition function used is bin_at() and isn't customizable.

Partition properties

Property Description Recommended value
RangeSize A timespan scalar constant that indicates the size of each datetime partition. Start with the value 1.00:00:00 (one day). Don't set a shorter value, because it may result in the table having a large number of small extents that can't be merged.
Reference A datetime scalar constant that indicates a fixed point in time, according to which datetime partitions are aligned. Start with 1970-01-01 00:00:00. If there are records in which the datetime partition key has null values, their partition value is set to the value of Reference.
OverrideCreationTime A bool indicating whether or not the result extent's minimum and maximum creation times should be overridden by the range of the values in the partition key. Defaults to false. Set to true if data isn't ingested in-order of time of arrival. For example, a single source file may include datetime values that are distant, and/or you may want to enforce retention or caching based on the datetime values rather than the time of ingestion.

When OverrideCreationTime is set to true, extents may be missed in the merge process. Extents are missed if their creation time is older than the Lookback period of the table's Extents merge policy. To make sure that the extents are discoverable, set the Lookback property to HotCache.

Uniform range datetime partition example

The snippet shows a uniform datetime range partition key over a datetime typed column named timestamp. It uses datetime(2021-01-01) as its reference point, with a size of 7d for each partition, and doesn't override the extents' creation times.

{
  "ColumnName": "timestamp",
  "Kind": "UniformRange",
  "Properties": {
    "Reference": "2021-01-01T00:00:00",
    "RangeSize": "7.00:00:00",
    "OverrideCreationTime": false
  }
}

The policy object

By default, a table's data partitioning policy is null, in which case data in the table won't be repartitioned after it's ingested.

The data partitioning policy has the following main properties:

  • PartitionKeys:

  • EffectiveDateTime:

    • The UTC datetime from which the policy is effective.
    • This property is optional. If it isn't specified, the policy will take effect for data ingested after the policy was applied.

Caution

  • You can set a datetime value in the past and partition already-ingested data. However, this practice may significantly increase resources used in the partitioning process.
  • In most cases, it is recommended to only have newly ingested data partitioned, and to avoid partitioning large amounts of historical data.
  • If you choose to partition historical data, consider doing so gradually, by setting the EffectiveDateTime to a previous datetime in steps of up to a few days each time you alter the policy.

Data partitioning example

Data partitioning policy object with two partition keys.

  1. A hash partition key over a string-typed column named tenant_id.
    • It uses the XxHash64 hash function, with MaxPartitionCount set to the recommended value 128, and the default Seed of 1.
  2. A uniform datetime range partition key over a datetime type column named timestamp.
    • It uses datetime(2021-01-01) as its reference point, with a size of 7d for each partition.
{
  "PartitionKeys": [
    {
      "ColumnName": "tenant_id",
      "Kind": "Hash",
      "Properties": {
        "Function": "XxHash64",
        "MaxPartitionCount": 128,
        "Seed": 1,
        "PartitionAssignmentMode": "Uniform"
      }
    },
    {
      "ColumnName": "timestamp",
      "Kind": "UniformRange",
      "Properties": {
        "Reference": "2021-01-01T00:00:00",
        "RangeSize": "7.00:00:00",
        "OverrideCreationTime": false
      }
    }
  ]
}

Additional properties

The following properties can be defined as part of the policy. These properties are optional and we recommend not changing them.

Property Description Recommended value Default value
MinRowCountPerOperation Minimum target for the sum of row count of the source extents of a single data partitioning operation. 0
MaxRowCountPerOperation Maximum target for the sum of the row count of the source extents of a single data partitioning operation. Set a value lower than 5M if you see that the partitioning operations consume a large amount of memory or CPU per operation. 0, with a default target of 5,000,000 records.
MaxOriginalSizePerOperation Maximum target for the sum of the original size (in bytes) of the source extents of a single data partitioning operation. If the partitioning operations consume a large amount of memory or CPU per operation, set a value lower than 5 GB. 0, with a default target of 5,368,709,120 bytes (5 GB).

The data partitioning process

  • Data partitioning runs as a post-ingestion background process.
    • A table that is continuously ingested into is expected to always have a "tail" of data that is yet to be partitioned (nonhomogeneous extents).
  • Data partitioning runs only on hot extents, regardless of the value of the EffectiveDateTime property in the policy.
    • If partitioning cold extents is required, you need to temporarily adjust the caching policy.

You can monitor the partitioning status of tables with defined policies in a database by using the .show database extents partitioning statistics command and partitioning metrics.

Partitioning capacity

::: moniker range = "azure-data-explorer"

  • To avoid consuming too many resources, these dynamic increases are capped. You may be required to gradually and linearly increase them beyond the cap, if they're used up entirely.
    • If increasing the capacities causes a significant increase in the use of the cluster's resources, you can scale the cluster up/out, either manually, or by enabling autoscale. ::: moniker-end

Limitations

  • Attempts to partition data in a database that already has more than 5,000,000 extents will be throttled.
    • In such cases, the EffectiveDateTime property of partitioning policies of tables in the database will be automatically delayed by several hours, so that you can reevaluate your configuration and policies.

Outliers in partitioned columns

  • The following situations can contribute to imbalanced distribution of data across nodes, and degrade query performance:
    • If a hash partition key includes values that are much more prevalent than others, for example, an empty string, or a generic value (such as null or N/A).
    • The values represent an entity (such as tenant_id) that is more prevalent in the dataset.
  • If a uniform range datetime partition key has a large enough percentage of values that are "far" from the majority of the values in the column, the overhead of the data partitioning process is increased and may lead to many small extents to keep track of. An example of such a situation is datetime values from the distant past or future.

In both of these cases, either "fix" the data, or filter out any irrelevant records in the data before or at ingestion time, to reduce the overhead of the data partitioning. For example, use an update policy.

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