Cloud-native, enterprise-level cron job platform for Kubernetes


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Furiko is a cloud-native, enterprise-level cron and adhoc job platform for Kubernetes.

The main website for documentation and updates is hosted at


Furiko is a Kubernetes-native operator for managing, scheduling and executing scheduled and adhoc jobs and workflows. It aims to be a general-purpose job platform that supports a diverse range of use cases, including cron jobs, batch processing, workflow automation, etc.

Furiko is built from the beginning to support enterprise-level use cases and running self-hosted in a private Kubernetes cluster, supporting users across a large organization.

Some use cases that are perfect for Furiko include:

  • Cron-based scheduling massive amounts of periodic jobs per day in a large organization
  • Scheduling some jobs to run once at a later time, with a set of specific inputs
  • Starting multiple jobs to execute one after another, once the previous job has finished
  • Event-driven, offline/asynchronous job processing via webhooks
  • Building a platform to automate business operations via form-based inputs (with Furiko as the job engine)




NOTE: Although started within the company, Furiko is not an official Shopee project or product.

Furiko is licensed under the Apache License, Version 2.0.

Logo is designed by Duan Weiwei, and is distributed under CC-BY 4.0.

Cloud-native, enterprise-level cron job platform for Kubernetes
  • feat(concurrency): Support variable MaxConcurrency

    feat(concurrency): Support variable MaxConcurrency

    Closes #16.

    Implements MaxConcurrency in ConcurrencySpec, which allows specifying a custom maximum concurrency value. This applies to both Forbid and Enqueue.

  • feat(execution): Add Indexes to ParallelStatus, update GetJobCommand

    feat(execution): Add Indexes to ParallelStatus, update GetJobCommand

    • Update ParallelStatus to contain list of Indexes.
    • Update furiko get job command:
      • Support --output detail and show all tasks and parallel task groups.
      • Show parallel task summary based on index counts.
      • Show job status task summary based on task counts.
    • Other changes:
      • Fix incorrect enum value for TaskTerminating.
      • Deprecate TaskPendingTimeout result, TaskKilled will be used instead, and PendingTimeout will be stored in the Reason.
      • Add more GetPhase unit tests.
  • feat(cli): Add enable/disable subcommands

    feat(cli): Add enable/disable subcommands

    Adds two new subcommands:

      furiko [command]
    Available Commands:
      disable     Disable automatic scheduling for a JobConfig.
      enable      Enable automatic scheduling for a JobConfig.

    Detailed subcommand help:

    Disables automatic scheduling for a JobConfig.
    If the specified JobConfig does not have a schedule, then an error will be thrown.
    If the specified JobConfig is already disabled, then this is a no-op.
      furiko disable [flags]
      # Disable scheduling for the JobConfig.
      furiko disable send-weekly-report
    Enables automatic scheduling for a JobConfig.
    If the specified JobConfig does not have a schedule, then an error will be thrown.
    If the specified JobConfig is already enabled, then this is a no-op.
      furiko enable [flags]
      # Enable scheduling for the JobConfig.
      furiko enable send-weekly-report
  • fix(webhook): Add default values for parallelism.completionStrategy

    fix(webhook): Add default values for parallelism.completionStrategy

    Bug Reproduction

    Trying to apply the following YAML results in not being able to save:

    kind: JobConfig
      name: jobconfig-parallel-sleep
        policy: Forbid
          expression: "H/15 * * * *"
          timezone: Asia/Singapore
        disabled: false
              - "30"
              - "60"
              - "120"
                  - name: job-container
                      - bash
                      - -c
                      - "echo Sleeping for ${task.index_key}; sleep ${task.index_key}"
                    image: bash

    The following error is displayed:

    $ k apply -f jobconfig-parallel-sleep.yaml
    The JobConfig "jobconfig-parallel-sleep" is invalid: spec.template.spec.parallelism.completionStrategy: Required value


    By right, we should also add default values for JobTemplate in JobConfigSpec. The default value should be set to AllSuccessful.

  • feat(execution): Avoid using activeDeadlineSeconds to kill tasks

    feat(execution): Avoid using activeDeadlineSeconds to kill tasks

    Closes #64. Also helps #63.

    Avoids using activeDeadlineSeconds to kill tasks, instead it will use API deletion instead, which supports graceful termination.

  • feat(execution): Implement parallelism in Job

    feat(execution): Implement parallelism in Job

    Closes #71.

    This implements all necessary features to support parallel tasks in a single Job. The following changes are introduced:

    1. Added API changes to introduce ParallelismSpec according to the proposal in #71.
    2. Revamped API for JobStatus fields, and reduced the possible set of phase, results, states, etc. to reduce duplication.
    3. Changed the job and task naming convention to delimit name components with hyphens instead of periods (e.g. jobconfig-parallel-1653824280 and jobconfig-parallel-sleep-1653822660-ge3tgm-0)
    4. Added mutation and validation handlers for ParallelismSpec.
    5. Compute uncreated tasks and create them in reconciler.

    Remaining TODO items:

    • [x] Immediately terminate all remaining tasks when a single task fails (all retries exceeded) when using AllSuccessful
    • [x] Create reconciler integration tests
  • feat(api): Make PodTemplateSpec schemaless

    feat(api): Make PodTemplateSpec schemaless

    Addresses an issue mentioned in #63:

    Another solution without needing to use a string is to use type: object in the CRD definition without properties, which prevents any schema validation. We could consider doing this for the PodTemplateSpec field currently as well.

    By avoiding an embed of the core/v1 API types in the CRD's OpenAPI schema, we avoid a few problems:

    1. Unknown fields error/pruning of unknown fields when Furiko is embedding a higher API version than the API version of the apiserver
    2. Incorrect/incomplete schema from kubectl explain
    3. (unlikely) Support backwards-incompatible changes from one API version to the next

    Also fixes the issue that metav1.ObjectMeta fields (when not at the root of a CRD) will not be able to store non-string values, because for some reason, the apiserver treats it as map[string]string rather than an Object.

  • Proposal: Support task-level parallelism

    Proposal: Support task-level parallelism


    Users may wish to shard their periodic jobs into multiple Pods. For example every day at 12am, we will need to process a whole bunch of work, and this work to be done may significantly increase in volume over time, and the work cannot be done before 12am (i.e. on the previous day). At where we stand right now, the only option is to support vertical scaling of a Pod, which is obviously impractical beyond a certain point. As such, we want to evaluate how to support horizontal scaling of Job pods (i.e. task-level parallelism).

    The Job object in K8s currently supports parallel execution: However, it is my personal opinion that the K8s JobController API for controlling parallelism of multiple Pods is not very clear and well-designed. We will outline the use cases, and attempt to propose an API design that would support and potentially improve the existing one in K8s.

    It is also important to avoid over-designing this feature. A good principle to keep in mind is that Furiko's main focus on automatic timed/periodic tasks, not so much user-invoked or complex task workflows, and we are better off utilizing more complex workflow engines (i.e. Argo Workflows) to support them.

    Use Cases

    We will outline some use cases (including/highlighting those we have gotten internally):

    1. Support running a fixed number of Pods per Job at the same time: This would basically be equivalent to "scaling up" a Job to more replicas, but each Pod has to be assigned work independently of other Pods. This usually involves an external message queue.
      • The equivalent use case in K8s is "Parallel Jobs with a work queue".
      • The advantage is that there is a well-defined upper bound in the amount of resources required to run the Job, but the disadvantage is that any unexpected increase in work to be done could result in longer processing times.
    2. Support running a variable number of Pods per Job at the same time: This is an extension of (1), except that the parallelism factor is variable.
      • One idea could be to use (1) but allow changing the parallelism at both adhoc invocation time (prior to creation), and during runtime (after it is started) which could be controlled by an autoscaler of sorts. See (3) for more details on the latter.
      • If we allow the parallelism factor to depend on an external data source (e.g. Kafka topic lag), then it becomes dangerously close to a MapReduce design. I think it may be better to require the parallelism factor to be defined in the workload spec itself.
    3. Horizontal scaling of Pods while a Job is running: While a Job is running, we may want to update the number of Pod replicas if we realize that it may not complete in time without stopping its progress. (just an idea, no real use case for now)
      • This can be utilized by jobs which read from a queue with a central coordinator, but not so much when the number of shards is fixed. One notable exception is Kafka, where consumers can rebalance when new consumers are added to the consumer group, and scale up to a maximum of the number of partitions.
      • Implementing this should be straightforward, but we have to be careful about the scale-down case, since it may conflict with completion policies (see below). A simple way to get around this is to prevent scale-down, and only allow scale-up.
    4. Stateless/Stateful parallel worker patterns: When a Job has multiple parallel Pods, it could be possible that some Pods can pick up work from a queue such that other Pods don't need to do so, so it would be sufficient to terminate once any Pod is finished. On the other hand, if every Pod works on its subset or work and nothing else (e.g. using consistent hashing), then we need to wait for ALL Pods to finish before terminating. As such, we need to support both use cases.

    I personally don't think there is a need for "fixed completion count" jobs like in K8s, at least I have never encountered a use case which depends on this. Perhaps the close equivalent of "fixed completion count" is to start N Jobs at the same time with a fixed concurrency factor, which is slightly different from the topic we are currently discussing.


    1. The parallelism feature must not conflict with the retries feature of Jobs. In other words, the distinction between retries and parallel tasks should be clear. In the batch/v1 JobController, it depends on a Pod's restartPolicy to retry containers, but we explicitly support pod-level retries.
    2. Control the completion and success policy: The user may want to be explicit about what and when constitutes a successful or a completed Job. In the case of a single Pod, using exit code 0 (i.e. the Pod's phase should be Success) is sufficient to indicate a successful Job, but it becomes less clear once we have parallel Pods.
    3. Control early termination policies: When a single Pod fails, it could be possible that we want to immediately terminate early from the Job in order to avoid unnecessary work, or to wait for all Pods to gracefully finish their existing work.

    Proposed Implementation 1

    We are not going forward with this proposal.

    Show old proposal...

    We will implement a new CRD ShardedSet (NOTE: the name is currently TBC). This CRD is most similar to a ReplicaSet, except that it controls running to completion (which ironically actually makes it similar to batch/v1 Job itself).

    The implementation of a ShardedSet will follow closely to the Indexed Job pattern in batch/v1 (, but defines completion policy in a much more explicit manner than is currently supported by the batch/v1 Job API, and avoids the confusion with having to define completions. See for some related discussion about how completions are handled.

    CRD Design

    Example of a proposed ShardedSet custom resource object, with all possible API fields (including future expansion):

    kind: ShardedSet
      # Defines that exactly 5 tasks are run in parallel.
      # Each task receives a separate task index, and it is guaranteed that
      # no two concurrently running tasks will receive the same task index.
      parallelism: 5
      # Defines retry policy.
        # Maximum attempts per shard, beyond which we stop creating new tasks for the shard. Defaults to 1.
        maxAttemptsPerShard: 3
        # Cannot exceed maxAttemptsPerShard * parallelism (also the default).
        # If a shard fails but this is exceeded, then it is considered a shard failure.
        maxAttemptsTotal: 15
      # Defines completion policy.
        # Defines when a ShardedSet is completed. Options:
        #  - OnAllShardsSuccess (default): Stop once all shards are successful.
        #  - OnAnyShardSuccess: Stop once any shard is successful.
        #  - OnAnyShardFailure: Stop once any shard is failed.
        condition: OnAllShardsSuccess
        # Defines what to do on completion, depending on whether the ShardedSet is successful or failed, the defaults are shown below.
        # Note that this has no effect for OnAllShardsSuccess, since by definition all shards would have completed prior to taking this action.
        onSuccess: WaitForRemaining
        onFailure: TerminateRemaining
      # The TaskTemplateSpec itself, we could further compose other task executors too!
              - name: container
                image: alpine
                args: ["echo", "Hello world"]
                  # The task can determine its shard index using this env var.
                  - name: SHARD_INDEX
                    value: "${parallel.shard_index}"

    Complete breakdown for completion.condition success cases:

    • OnAllShardsSuccess
      • When some shard succeeds, do nothing.
      • When all shards succeed, succeed the ShardedSet.
    • OnAnyShardSuccess
      • When some shard succeeds, immediately succeed the ShardedSet.
    • OnAnyShardFailure
      • When some shard succeeds, do nothing.
      • When all shards succeed, succeed the ShardedSet.

    Complete breakdown for completion.condition failure cases:

    • OnAllShardsSuccess
      • If any shard cannot retry further (exceed maxAttempts), immediately fail the ShardedSet.
    • OnAnyShardSuccess
      • If any shard cannot retry further (exceed maxAttempts), do nothing.
      • If all shards failed and cannot retry further (exceed maxAttempts), fail the ShardedSet.
    • OnAnyShardFailure
      • If any shard cannot retry further (exceed maxAttempts), immediately fail the ShardedSet.

    Note that:

    • In the success case, OnAllShardsSuccess == OnAnyShardFailure
    • In the failure case: OnAllShardsSuccess == OnAnyShardFailure

    Therefore, we can simplify it to just AllShardsSucceeded and AnyShardSucceeded. (Help me verify this claim??)

    Inside a JobConfig, the user will define it like this:

    kind: JobConfig
          # Retry the ShardedSet up to 3 times
          maxAttempts: 3
              # This is the start of the ShardedSetTemplateSpec
                  labels: ...
                  parallelism: 5
                        containers: [ ... ]

    Pros and Cons

    • Pros:
      • Very easy to reason about. The composition of two separate APIs is clear from a developer and a user perspective, and future extensions to the ShardedSet controller avoids conflicting with the core JobController.
      • Most users will not need to think about the additional API fields that are introduced for parallelism if they don't need it. In my opinion, this is the biggest issue with the batch/v1 Job.
    • Cons:
      • By composing a secondary task executor to achieve task-level parallelism, we may be prematurely confining the design to only support a subset of use cases. For example, by separating the retry and parallel sets into distinct layers we may constrain the possible expansion options in the future.
      • Additional implementation cost, but it is saved by reducing the work on ensuring that the existing JobController behavior is not broken if we do Option 2.
      • Potentially duplicate logic in both ShardedSet and Job controllers (e.g. task adoption, task executor).

    Proposed Implementation 2

    Another way is to avoid the use of a CRD, but implement it directly in the JobController and update the Job API.

    We will add the following fields to the spec:

          withCount: 3
          completionStrategy: AllSuccessful

    Some possible parallelism types:

    • withCount: Specify absolute number, the index number will be generated from 0 - N-1 in ${task.index_num}
    • withKeys: Specify a list of string keys, it will be made available in ${task.index_key}
    • withMatrix: Specify a map of keys to a list of string values, each key will be available in ${task.index_matrix.<matrix_key>}. This is to support common parallel patterns (e.g. CI on multiple platform and version combinations)

    Some considerations:

    1. Retries will take place on a parallel index-level. This means that using withCount of 3, each index (0, 1, 2) has a maximum of 3 retries each.
    2. The completionStrategy is similar to Proposal (1).

    The main reason we are not using Proposal (1) is because of the complexity introduced when having nested retries, and it is actually clearer to inline the implementation into the same CRD/controller.


    There are some alternatives to the above design to achieve the same requirements.

    1. Creating one JobConfig for each desired shard. The obvious downside is that you have duplicate objects and higher cost of maintenance, configuration drift, etc.
    2. Support starting multiple Jobs at each schedule. This is a very simple solution, but there are some drawbacks:
      • Each Job started at the same time are basically independent of each other, and we cannot determine the status or control the workload as a single atomic unit.
      • Multiple Jobs started concurrently that spill over their normal run duration may eat into the maxConcurrency of the JobConfig (see #16), resulting in lesser total Jobs being run than expected.

    TODO List

    • #81
    • #83
  • chore: Move console to separate package

    chore: Move console to separate package

    The package does not support Windows:

    ../../../../pkg/mod/[email protected]/vt_other.go:26:8: t.cur.attr undefined (type Cursor has no field or method attr, but does have Attr)
    ../../../../pkg/mod/[email protected]/vt_other.go:27:8: t.cur.attr undefined (type Cursor has no field or method attr, but does have Attr)

    Since we only use it for unit tests, we should chuck it into its own package so that it does not need to be imported when building the CLI.

  • feat(cli): Initial implementation of furiko-cli

    feat(cli): Initial implementation of furiko-cli

    Partially implements #67.

    This is an initial implementation of Furiko's CLI. We currently support only the following commands:

    • furiko get job
    • furiko get jobconfig
    • furiko list job
    • furiko list jobconfig
    • furiko run

    We don't yet support create (from file, which is needed for JobConfigs and independent Jobs), edit resources, and watch resources. But in many cases, this is identical to kubectl so we are not really adding much value here. Additionally, we don't yet have a way to test the CLI, although it should be feasible in the future.

  • Feature: Implement furiko CLI

    Feature: Implement furiko CLI

    We will implement a basic command line interface (CLI) utility furiko, which provides a kubectl-like interface to interact with a Kubernetes API server.

    The goals of the CLI tool include:

    1. Providing shortcuts to simplify and streamline commonly performed actions.
    2. Providing a TUI (terminal user interface) when appropriate, for example prompting for option values before executing a JobConfig.

    The CLI should NOT be a full replacement of kubectl, but it should be sufficient for 80-90% of users' use cases. They can dig into kubectl if they need to.

  • Controller crash when deleting a JobConfig

    Controller crash when deleting a JobConfig

    Observed panic when deleting a JobConfig that has no schedule set:

    panic: time: missing Location in call to Time.In
    goroutine 259 [running]:
            /usr/local/go/src/time/time.go:1105*Schedule).Bump(0xc0004d12f0?, 0xc00060cf00?, {0x40d425?, 0x7f81f46e2b00?, 0x29e0220?})
            /go/src/ +0x5f9*CronWorker).refreshUpdatedJobConfigs(0xc00003a780, {0x1a96bb4?, 0x22?, 0x29e0220?})
            /go/src/ +0x2bf*CronWorker).Work(0xc00003a780)
            /go/src/ +0x258
            /go/src/ +0x62, 0x0?, 0xc00041d0e0)
            /go/src/ +0x247
    created by*CronWorker).Start
            /go/src/ +0x12a
  • feat(taskexecutor): Implement Argo Workflows task executor

    feat(taskexecutor): Implement Argo Workflows task executor

    Implements a new task executor: Argo Workflows.

    Currently, the task executor is very simple:

    1. One task = one Workflow. This means that retries (at Furiko level) will create a new Workflow, and parallelism (at Furiko level) will have multiple concurrent Workflows.
    2. Most task fields would have an equivalent meaning in Argo Workflows.
    • RunningTimestamp: workflow.status.startedAt (will be set once non-pending)
    • FinishTimestamp: workflow.status.finishedAt
    1. Substitution only occurs in .spec.arguments.parameters.*.value. This avoids conflicting with Argo's own substitution mechanism.
    2. If the Argo Workflow CRD is not installed, Furiko will not watch or support the argoWorkflow task executor. If it is installed or uninstalled, currently it would require a restart of execution-controller to take effect, because we need to reinitialize the informers.
    • Users can still create JobConfigs with argoWorkflow task executor, but a warning will be returned by the admission webhook.
    • When trying to run a new Job with the argoWorkflow task executor, AdmissionRefused will be thrown if it cannot be created because the CRD is not installed.
  • Migrate to

    Migrate to


    Internally we upgraded to use v0.24.x of client-go (was previously on v0.20.9), and found that some usages of is not 100% compatible with the now-preferred

    It seems that v0.23.0 is still okay, but we should migrate it still.

  • Bug: Changing defaultPendingTimeoutSeconds should not affect already created jobs

    Bug: Changing defaultPendingTimeoutSeconds should not affect already created jobs

    When updating the defaultPendingTimeoutSeconds, any already ongoing jobs will be affected by the change. This is probably undesirable, especially if the cluster administrator shortens the timeout from a longer one to a shorter one, it may cause previously created jobs to be killed. We should uphold a principle such that old resources should NOT be affected by newly updated defaults.

    As such, we should add pendingTimeoutSeconds via the JobMutatingWebhook if it is not specified, so that the controller does not wrongly use the new global configuration.

  • Feature: Support NotDuring constraints for JobConfig

    Feature: Support NotDuring constraints for JobConfig

    Users may want to have a periodically scheduled job that runs regularly on interval, except for some explicitly defined time ranges. For example, we may want to specify that during deploy freezes or some other real-world event, a cron job should not be run.

    Users should be able to specify a list of time ranges (start and end time, inclusive), during which schedules are not allowed.

    API Design

    kind: JobConfig
      name: my-job-config
      namespace: my-namespace
        # Schedule every hour from 10AM to 6PM.
          expression: 0 10-18 * * *
          timezone: Asia/Singapore
          # Example of multiple notDuring freeze periods, inclusive.
            - start: 2022-04-01T00:00:00+08:00
              end: 2022-04-02T11:59:59+08:00
            - start: 2022-04-28T00:00:00+08:00
              end: 2022-04-28T15:59:59+08:00

    Using the above example:

    • On 1st April, no schedules will be created at all.
    • On 2nd April, the first schedule that day will be at 12:00:00.
    • On 28th April, the first schedule that day will be at 16:00:00.

    Possible Extensions

    • Sharing constraints across multiple JobConfigs
    • Regular constraints instead of fixed time range: Using a cron expression to define exclusions to the main cron schedule, similar to GitLab
  • Enhancement: CronController can avoid back-scheduling for Forbid

    Enhancement: CronController can avoid back-scheduling for Forbid

    Since we have updated to use the JobQueueController instead, the process of admitting a Job to be started is now asynchronous. The CronController will back-schedule multiple Jobs even though it should know at this point to just ignore the back-scheduling for ConcurrencyPolicyForbid, because the JobQueueController will just reject them as AdmissionError:

    NAME                          AGE   PHASE            CREATED TASKS   RUN TIME   FINISH TIME
    jobconfig-sample.1650055799   7s    Succeeded        1                          3s
    jobconfig-sample.1650055814   7s    AdmissionError   0                          7s
    jobconfig-sample.1650055829   7s    AdmissionError   0                          7s
    jobconfig-sample.1650055844   7s    AdmissionError   0                          7s
    jobconfig-sample.1650055859   7s    AdmissionError   0                          7s

    In case users set a high back-scheduling limit, this may result in huge bursts of unnecessary Job creation.

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