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MidPoint 3.8 and later

Starting with midPoint 3.8, the tasks module provides the following functionality:

  1. A task can distribute the work to multiple nodes at once (not only to multiple threads as it was up to midPoint 3.7.x).
  2. A task can be resumed at the place where it was suspended (not always from the beginning as it was up to midPoint 3.7.x).

This is implemented using bucket-based work state management along with configurable task partitioning.

In this article we describe the overall picture and the details of work segmentation definition. Workers management and task partitioning are the topics of separate ones.

Bucket-based work state management

Work buckets

The work is divided into buckets - abstract chunks of work.

Usually the work consists of iteration over a set of objects: either stored in midPoint repository (e.g. for recomputation task) or stored on a resource (e.g. import, reconciliation or live synchronization). So, the most natural way of segmentation of the work into buckets is by defining a bucket as a set of objects for which a particular item - let us call it discriminator - has a value in a given interval. The interval can be numeric, alphanumeric, or of anything comparable (e.g. timestamps). In the future, OIDs can be used for segmentation as well.

There are other possibilities as well. For example, one could segment users according to employee type, organization membership, and so on. Work buckets can be defined using arbitrary search filter(s) over the set of objects.

Basic bucket state

A major distinction for a work bucket is: is it complete or not? The work bucket is declared complete if there's no work that can be done on it. It does not mean that all the objects were successfully processed, though. Some of them might incur failures; however, this is considered a normal situation and such objects are treated as processed. (Re-processing of such objects can be implemented in the future, if needed.) The other state is ready meaning that there is some part of the bucket (maybe all of it) that needs to be processed.

Buckets are kept in the task workState data structure. This allows us to track the progress done (at coarse-grained level), restarting the work on last known point if necessary.

Multi-node work distribution

Buckets allow us not only to track the progress, but to easily distribute the work among multiple worker tasks, with the intention of their distribution among cluster nodes.

For such multi-node scenario there is a coordinator task and worker tasks. Coordinator holds the authoritative list of buckets to be processed. Each worker tries to grab one or more buckets to work on. Such buckets are then copied from the coordinator's workState into the worker's one. To know they were allocated their state in coordinator's state is marked as delegated. (This is the third possible state besides ready and complete). After the bucket is processed, it is removed from worker's workState and marked in coordinator's workState as complete.

Minor bucket state a.k.a. bucket progress (future plans)

This is only an idea of a future work.

Sometimes we want to be able to track the progress in more details to avoid needless re-processing objects from the start of the current bucket to the place where the processing stopped. (This might be crucial for situations where the whole processing consists of a single bucket.)

Most typical way how to track in-bucket progress is to:

  1. sort processed objects by some progress-tracking property (OID, icfs:name, icfs:uid, or basically anything);
  2. remember last processed object's progress-tracking property value.

Note that object ordering is not required to manage major bucket state. Nor must the property used for bucket segmentation (discriminator) be the same as minor progress-tracking property - although they will be probably the same for the majority of cases.

Configuring work segmentation into buckets

The following structure is used (embedded in task's workManagement item):


Kind of task with respect to the work state management:

  • standalone: the task is self-contained. It maintains the list of buckets and process all of them itself.
  • coordinator: the task maintains the list of buckets, but the work is done by its worker subtasks.
  • worker: this is the subtask that performs the work.

Besides these values, there is also partitionedMaster value that denotes a compound task that is partitioned into subtasks, each carrying out a specific step (stage) of the processing.

bucketsHow buckets are created, delegated, completed, how they are translated into objects for processing.
workersHow workers are created and managed. This is applicable only to tasks of coordinator kind.
partitionsHow subtasks for individual partitions are created and managed. This is applicable only to tasks of partitionedMaster kind.

Bucket segmentation definition

An example

The segmentation is defined like this:

An example of segmentation configuration

This is to be read such that that the discriminator (ri:uid attribute) is expected to have a numeric value from 0 to 99999 (inclusive) and we want do divide this range into 100 buckets. So the first one will contain values from 0 to 999, second one from 1000 to 1999, then 2000-2999, etc. And the last one (100th) will contain values from 99000 to 99999, inclusive.

Definition options

Current implementation supports the following segmentation definitions:

Segmentation definitionParametersDescription
all definitionsdiscriminatorItem whose values will used to segment objects into buckets (if applicable). Usually required.
matchingRuleMatching rule to be applied when creating filters (if applicable). Optional.
numberOfBucketsNumber of buckets to be created (if applicable). Optional.
numericSegmentationfromStart of the processing space (inclusive). If omitted, 0 is assumed.

End of the processing space (exclusive). If not present, both bucketSize and numberOfBuckets must be defined and the end of processing space is determined as their product. In the future we might implement dynamic determination of this value e.g. by counting objects to be processed.


Size of one bucket. If not present it is computed as the total processing space divided by number of buckets (i.e. to and numberOfBuckets must be present).


Characters that make up the prefix or interval. Currently, the string segmentation is done by creating all possible boundaries (by combining boundaryCharacters) and then using these boundaries either as interval boundaries (if comparisonMethod is interval) or as prefixes (if comparisonMethod is prefix).

This is a multivalued property: the first value contains characters that occupy the first place in the boundary. The second value contains characters destined for the second place, etc.

An example: if boundaryCharacters = ("qx", "0123456789", "0123456789", "0123456789") then the following boundaries are generated: q000, q001, q002, ..., q999, x000, x001, ..., x999. This might be suitable e.g. for accounts that start either with "q" or with "x" and then continue with numbers, like q732812.

Another example: if boundaryCharacters = ("abcdefghijklmnopqrstuvwxyz", "0123456789abcdefghijklmnopqrstuvwxyz") then the following boundaries are generated: a0, a1, a2, ..., a9, aa, ab, ..., az, b0, b1, ..., b9, ba, ..., bz, ..., z0, z1, ..., z9, za, ..., zz. This might be suitable e.g. for alphanumeric account names that always start with alphabetic character.

Beware: current implementation requires that the characters are specified in the order that complies with the matching rule used. Otherwise, empty intervals might be generated, like when using "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ" there will be an interval of e.g. "values greater than z but lower than A" (empty one) or "values greater than Z" (covers items covered by earlier intervals of a-b, b-c, ...).


If a value N greater than 1 is specified here, boundaryCharacters values are repeated N times. This means that if values of V1, V2, ..., Vk are specified, the resulting sequence is V1, V2, ..., Vk, V1, V2, ..., Vk etc, with N repetitions - so Nk values in total.

comparisonMethodEither interval (the default), resulting in interval queries like item >= 'a' and item < 'b'. Or prefix, resulting in prefix queries like item starts with 'a'. Beware, when using prefix method, all the discriminator values are covered by boundaryCharacters you specify. Otherwise some items will not be processed at all.
oidSegmentationThe same as stringSegmentation but providing defaults of discriminator# and boundaryCharacters0-9a-f (repeated depth times, if needed).
explicitSegmentationcontentExplicit content of work buckets to be used. This is useful e.g. when dealing with filter-based buckets. But any other bucket content (e.g. numeric intervals, string intervals, string prefixes) might be used here as well.

More examples

The oidSegmentation is the easiest one to be used when dealing with repository objects. The following creates 162 = 256 segments.

Buckets defined on first two characters of the OID

The following configuration provides string interval buckets:

  • less than a
  • greater or equal a, less than b
  • greater or equal b, less than c
  • ...
  • greater or equal y, less than z
  • greater or equal z

(comparison is done on normalized form of the name attribute)

Buckets defined on the first character of the name

The following configuration provides three buckets. The first comprises identifier values less than 123. The second comprises values from 123 (inclusive) to 200 (exclusive). And the last one contains values greater than or equal to 200.

Three work buckets defined as numeric intervals

The following configuration provides four buckets. The first three correspond to users with employeeType of teacherstudent and administrative. The last one corresponds to user with no employeeType set.

Work buckets defined on employeeType values
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