zendern/db-scheduler

Persistent cluster-friendly scheduler for Java

db-scheduler

Task-scheduler for Java that was inspired by the need for a clustered java.util.concurrent.ScheduledExecutorService simpler than Quartz.

As such, also appreciated by users (cbarbosa2, rafaelhofmann):

Your lib rocks! I'm so glad I got rid of Quartz and replaced it by yours which is way easier to handle!

cbarbosa2

See also why not Quartz?

Features

• Cluster-friendly. Guarantees execution by single scheduler instance.
• Persistent tasks. Requires a single database-table for persistence.
• Embeddable. Built to be embedded in existing applications.
• High throughput. Tested to handle 2k - 10k executions / second. Link.
• Simple.
• Minimal dependencies. (slf4j)

Table of contents

• Getting started
• Who uses db-scheduler?
• Examples
• Configuration
• Third-party task repositories
• Spring Boot usage
• Interacting with scheduled executions using the SchedulerClient
• How it works
• Performance
• Versions / upgrading
• FAQ

Getting started

1. Add maven dependency

<dependency>
    <groupId>com.github.kagkarlsson</groupId>
    <artifactId>db-scheduler</artifactId>
    <version>10.2</version>
</dependency>

2. Create the scheduled_tasks table in your database-schema. See table definition for postgresql, oracle, mssql or mysql.

3. Instantiate and start the scheduler, which then will start any defined recurring tasks.

RecurringTask<Void> hourlyTask = Tasks.recurring("my-hourly-task", FixedDelay.ofHours(1))
        .execute((inst, ctx) -> {
            System.out.println("Executed!");
        });

final Scheduler scheduler = Scheduler
        .create(dataSource)
        .startTasks(hourlyTask)
        .threads(5)
        .build();

// hourlyTask is automatically scheduled on startup if not already started (i.e. exists in the db)
scheduler.start();

For more examples, continue reading. For details on the inner workings, see How it works. If you have a Spring Boot application, have a look at Spring Boot Usage.

Who uses db-scheduler?

List of organizations known to be running db-scheduler in production:

Company	Description
Digipost	Provider of digital mailboxes in Norway
Vy Group	One of the largest transport groups in the Nordic countries.
TransferWise	A cheap, fast way to send money abroad.
Becker Professional Education
Monitoria	Website monitoring service.

Feel free to open a PR to add your organization to the list.

Examples

See also runnable examples.

Recurring task

Define a recurring task and schedule the task's first execution on start-up using the startTasks builder-method. Upon completion, the task will be re-scheduled according to the defined schedule (see pre-defined schedule-types).

RecurringTask<Void> hourlyTask = Tasks.recurring("my-hourly-task", FixedDelay.ofHours(1))
        .execute((inst, ctx) -> {
            System.out.println("Executed!");
        });

final Scheduler scheduler = Scheduler
        .create(dataSource)
        .startTasks(hourlyTask)
        .registerShutdownHook()
        .build();

// hourlyTask is automatically scheduled on startup if not already started (i.e. exists in the db)
scheduler.start();

One-time tasks

An instance of a one-time task has a single execution-time some time in the future (i.e. non-recurring). The instance-id must be unique within this task, and may be used to encode some metadata (e.g. an id). For more complex state, custom serializable java objects are supported (as used in the example).

Define a one-time task and start the scheduler:

OneTimeTask<MyTaskData> myAdhocTask = Tasks.oneTime("my-typed-adhoc-task", MyTaskData.class)
        .execute((inst, ctx) -> {
            System.out.println("Executed! Custom data, Id: " + inst.getData().id);
        });

final Scheduler scheduler = Scheduler
        .create(dataSource, myAdhocTask)
        .registerShutdownHook()
        .build();

scheduler.start();

... and then at some point (at runtime), an execution is scheduled using the SchedulerClient:

// Schedule the task for execution a certain time in the future and optionally provide custom data for the execution
scheduler.schedule(myAdhocTask.instance("1045", new MyTaskData(1001L)), Instant.now().plusSeconds(5));

More examples

• EnableImmediateExecutionMain.java
• MaxRetriesMain.java
• ExponentialBackoffMain.java
• ExponentialBackoffWithMaxRetriesMain.java
• TrackingProgressRecurringTaskMain.java
• SpawningOtherTasksMain.java
• SchedulerClientMain.java
• PersistentDynamicScheduleMain.java

Configuration

Scheduler configuration

The scheduler is created using the Scheduler.create(...) builder. The builder has sensible defaults, but the following options are configurable.

Consider tuning

⚙️ .threads(int)
Number of threads. Default 10.

⚙️ .pollingInterval(Duration)
How often the scheduler checks the database for due executions. Default 10s.

⚙️ .enableImmediateExecution()
If this is enabled, the scheduler will attempt to directly execute tasks that are scheduled to now(), or a time in the past. For this to work reliably, the call to schedule(..) should not occur from within a transaction, because the record may not yet be visible to the scheduler (if this is a requirement, see the method scheduler.triggerCheckForDueExecutions()). Default false.

⚙️ .registerShutdownHook()
Registers a shutdown-hook that will call Scheduler.stop() on shutdown. Stop should always be called for a graceful shutdown and to avoid dead executions.

⚙️ .shutdownMaxWait(Duration)
How long the scheduler will wait before interrupting executor-service threads. If you find yourself using this, consider if it is possible to instead regularly check executionContext.getSchedulerState().isShuttingDown() in the ExecutionHandler and abort long-running task. Default 30min.

Polling strategy

If you are running >1000 executions/s you might want to use the lock-and-fetch polling-strategy for lower overhead and higher througput (read more). If not, the default fetch-and-lock-on-execute will be fine.

⚙️ .pollUsingFetchAndLockOnExecute(double, double)
Use default polling strategy fetch-and-lock-on-execute.
If the last fetch from the database was a full batch (executionsPerBatchFractionOfThreads), a new fetch will be triggered when the number of executions left are less than or equal to lowerLimitFractionOfThreads * nr-of-threads. Fetched executions are not locked/picked, so the scheduler will compete with other instances for the lock when it is executed. Supported by all databases.
Defaults: 0,5, 3.0

⚙️ .pollUsingLockAndFetch(double, double)
Use polling strategy lock-and-fetch which uses select for update .. skip locked for less overhead.
If the last fetch from the database was a full batch, a new fetch will be triggered when the number of executions left are less than or equal to lowerLimitFractionOfThreads * nr-of-threads. The number of executions fetched each time is equal to (upperLimitFractionOfThreads * nr-of-threads) - nr-executions-left. Fetched executions are already locked/picked for this scheduler-instance thus saving one UPDATE statement.
For normal usage, set to for example 0.5, 1.0.
For high throughput (i.e. keep threads busy), set to for example 1.0, 4.0. Currently hearbeats are not updated for picked executions in queue (applicable if upperLimitFractionOfThreads > 1.0). If they stay there for more than 4 * hearbeat-interval (default 20m), not starting execution, they will be detected as dead and likely be unlocked again (determined by DeadExecutionHandler). Currently supported by postgres.

Less commonly tuned

⚙️ .heartbeatInterval(Duration)
How often to update the heartbeat timestamp for running executions. Default 5m.

⚙️ .schedulerName(SchedulerName)
Name of this scheduler-instance. The name is stored in the database when an execution is picked by a scheduler. Default <hostname>.

⚙️ .tableName(String)
Name of the table used to track task-executions. Change name in the table definitions accordingly when creating the table. Default scheduled_tasks.

⚙️ .serializer(Serializer)
Serializer implementation to use when serializing task data. Default standard Java serialization.

⚙️ .executorService(ExecutorService)
If specified, use this externally managed executor service to run executions. Ideally the number of threads it will use should still be supplied (for scheduler polling optimizations). Default null.

⚙️ .deleteUnresolvedAfter(Duration)
The time after which executions with unknown tasks are automatically deleted. These can typically be old recurring tasks that are not in use anymore. This is non-zero to prevent accidental removal of tasks through a configuration error (missing known-tasks) and problems during rolling upgrades. Default 14d.

⚙️ .jdbcCustomization(JdbcCustomization)
db-scheduler tries to auto-detect the database used to see if any jdbc-interactions need to be customized. This method is an escape-hatch to allow for setting JdbcCustomizations explicitly. Default auto-detect.

⚙️ .commitWhenAutocommitDisabled(boolean)
By default no commit is issued on DataSource Connections. If auto-commit is disabled, it is assumed that transactions are handled by an external transaction-manager. Set this property to true to override this behavior and have the Scheduler always issue commits. Default false.

⚙️ .failureLogging(Level, boolean)
Configures how to log task failures, i.e. Throwables thrown from a task execution handler. Use log level OFF to disable this kind of logging completely. Default WARN, true.

Task configuration

Tasks are created using one of the builder-classes in Tasks. The builders have sensible defaults, but the following options can be overridden.

Option	Default	Description
.onFailure(FailureHandler)	see desc.	What to do when a ExecutionHandler throws an exception. By default, Recurring tasks are rescheduled according to their Schedule one-time tasks are retried again in 5m.
.onDeadExecution(DeadExecutionHandler)	ReviveDeadExecution	What to do when a dead executions is detected, i.e. an execution with a stale heartbeat timestamp. By default dead executions are rescheduled to now().
.initialData(T initialData)	null	The data to use the first time a recurring task is scheduled.

Schedules

The library contains a number of Schedule-implementations for recurring tasks. See class Schedules.

Schedule	Description
.daily(LocalTime ...)	Runs every day at specified times. Optionally a time zone can be specified.
.fixedDelay(Duration)	Next execution-time is Duration after last completed execution. Note: This Schedule schedules the initial execution to Instant.now() when used in startTasks(...)
.cron(String)	Spring-style cron-expression.

Another option to configure schedules is reading string patterns with Schedules.parse(String).

The currently available patterns are:

Pattern	Description
FIXED_DELAY|Ns	Same as .fixedDelay(Duration) with duration set to N seconds.
DAILY|12:30,15:30...(|time_zone)	Same as .daily(LocalTime) with optional time zone (e.g. Europe/Rome, UTC)

More details on the time zone formats can be found here.

Third-party task repositories

Out of the box db-scheduler supports jdbc-compliant databases. There have however been efforts to implement support for more databases via custom task repositories. It is currently a bit cumbersome plugging in a custom repository, but there are plans for making it easier. This is a list of known third-party task repositories:

• db-scheduler-mongo

Spring Boot usage

For Spring Boot applications, there is a starter db-scheduler-spring-boot-starter making the scheduler-wiring very simple. (See full example project).

Prerequisites

• An existing Spring Boot application
• A working DataSource with schema initialized. (In the example HSQLDB is used and schema is automatically applied.)

Getting started

1. Add the following Maven dependency

   <dependency>
       <groupId>com.github.kagkarlsson</groupId>
       <artifactId>db-scheduler-spring-boot-starter</artifactId>
       <version>10.2</version>
   </dependency>

   NOTE: This includes the db-scheduler dependency itself.
2. In your configuration, expose your Task's as Spring beans. If they are recurring, they will automatically be picked up and started.
3. If you want to expose Scheduler state into actuator health information you need to enable db-scheduler health indicator. Spring Health Information.
4. Run the app.

Configuration options

Configuration is mainly done via application.properties. Configuration of scheduler-name, serializer and executor-service is done by adding a bean of type DbSchedulerCustomizer to your Spring context.

# application.properties example showing default values

db-scheduler.enabled=true
db-scheduler.heartbeat-interval=5m
db-scheduler.polling-interval=10s
db-scheduler.polling-limit=
db-scheduler.table-name=scheduled_tasks
db-scheduler.immediate-execution-enabled=false
db-scheduler.scheduler-name=
db-scheduler.threads=10
# Ignored if a custom DbSchedulerStarter bean is defined
db-scheduler.delay-startup-until-context-ready=false

Interacting with scheduled executions using the SchedulerClient

It is possible to use a SchedulerClient to interact with the persisted future executions. While the Scheduler implements this interface, it is also possible to instantiate the client without the full scheduler-instance, i.e. without the executing part.

SchedulerClient.Builder.create(dataSource, taskDefinitions).build()

It will allow for operations such as:

• List scheduled executions
• Reschedule a specific execution
• Remove an old executions that have been retrying for too long
• ...

How it works

A single database table is used to track future task-executions. When a task-execution is due, db-scheduler picks it and executes it. When the execution is done, the Task is consulted to see what should be done. For example, a RecurringTask is typically rescheduled in the future based on its Schedule.

Optimistic locking is used to guarantee that a one and only one scheduler-instance gets to pick a task-execution.

Recurring tasks

The term recurring task is used for tasks that should be run regularly, according to some schedule (see Tasks.recurring(..)).

When the execution of a recurring task has finished, a Schedule is consulted to determine what the next time for execution should be, and a future task-execution is created for that time (i.e. it is rescheduled). The time chosen will be the nearest time according to the Schedule, but still in the future.

To create the initial execution for a RecurringTask, the scheduler has a method startTasks(...) that takes a list of tasks that should be "started" if they do not already have an existing execution. The initial execution-time is determined by the Schedule. If the task already has a future execution (i.e. has been started at least once before), but an updated Schedule now indicates another execution-time, the existing execution will be rescheduled to the new execution-time (with the exception of non-deterministic schedules such as FixedDelay where new execution-time is further into the future).

One-time tasks

The term one-time task is used for tasks that have a single execution-time (see Tasks.oneTime(..)). In addition to encode data into the instanceIdof a task-execution, it is possible to store arbitrary binary data in a separate field for use at execution-time. By default, Java serialization is used to marshal/unmarshal the data.

Custom tasks

For tasks not fitting the above categories, it is possible to fully customize the behavior of the tasks using Tasks.custom(..).

Use-cases might be:

• Recurring tasks that needs to update its data
• Tasks that should be either rescheduled or removed based on output from the actual execution

Dead executions

During execution, the scheduler regularly updates a heartbeat-time for the task-execution. If an execution is marked as executing, but is not receiving updates to the heartbeat-time, it will be considered a dead execution after time X. That may for example happen if the JVM running the scheduler suddenly exits.

When a dead execution is found, the Taskis consulted to see what should be done. A dead RecurringTask is typically rescheduled to now().

Performance

While db-scheduler initially was targeted at low-to-medium throughput use-cases, it handles high-throughput use-cases (1000+ executions/second) quite well due to the fact that its data-model is very simple, consisting of a single table of executions. To understand how it will perform, it is useful to consider the SQL statements it runs per batch of executions.

Polling strategy fetch-and-lock-on-execute

The original and default polling strategy, fetch-and-lock-on-execute, will do the following:

1. select a batch of due executions
2. For every execution, on execute, try to update the execution to picked=true for this scheduler-instance. May miss due to competing schedulers.
3. If execution was picked, when execution is done, update or delete the record according to handlers.

In sum per batch: 1 select, 2 * batch-size updates (excluding misses)

Polling strategy lock-and-fetch

In v10, a new polling strategy (lock-and-fetch) was added. It utilizes the fact that most databases now have support for SKIP LOCKED in SELECT FOR UPDATE statements (see 2ndquadrant blog). Using such a strategy, it is possible to fetch executions pre-locked, and thus getting one statement less:

1. select for update .. skip locked a batch of due executions. These will already be picked by the scheduler-instance.
2. When execution is done, update or delete the record according to handlers.

In sum per batch: 1 select-and-update, 1 * batch-size updates (no misses)

Benchmark test

To get an idea of what to expect from db-scheduler, see results from the tests run in GCP below. Tests were run with a few different configurations, but each using 4 competing scheduler-instances running on separate VMs. TPS is the approx. transactions per second as shown in GCP.

	Throughput fetch (ex/s)	TPS fetch (estimates)	Throughput lock-and-fetch (ex/s)	TPS lock-and-fetch (estimates)
Postgres 4core 25gb ram, 4xVMs(2-core)
20 threads, lower 4.0, upper 20.0	2000	9000	10600	11500
100 threads, lower 2.0, upper 6.0	2560	11000	11200	11200
Postgres 8core 50gb ram, 4xVMs(4-core)
50 threads, lower: 0.5, upper: 4.0	4000	22000	11840	10300

Observations for these tests:

• For fetch-and-lock-on-execute
  • TPS ≈ 4-5 * execution-throughput. A bit higher than the best-case 2 * execution-throughput, likely due the inefficiency of missed executions.
  • throughput did scale with postgres instance-size, from 2000 executions/s on 4core to 4000 executions/s on 8core
• For lock-and-fetch
  • TPS ≈ 1 * execution-throughput. As expected.
  • seem to consistently handle 10k executions/s for these configurations
  • throughput did not scale with postgres instance-size (4-8 core), so bottleneck is somewhere else

Currently, polling strategy lock-and-fetch is implemented only for Postgres. Contributions adding support for more databases are welcome.

Things to note / gotchas

• There are no guarantees that all instants in a schedule for a RecurringTask will be executed. The Schedule is consulted after the previous task-execution finishes, and the closest time in the future will be selected for next execution-time. A new type of task may be added in the future to provide such functionality.

• The methods on SchedulerClient (schedule, cancel, reschedule) and the CompletionHandler will run using a new Connectionfrom the DataSourceprovided. To have the action be a part of a transaction, it must be taken care of by the DataSourceprovided, for example using something like Spring's TransactionAwareDataSourceProxy.

• Currently, the precision of db-scheduler is depending on the pollingInterval (default 10s) which specifies how often to look in the table for due executions. If you know what you are doing, the scheduler may be instructed at runtime to "look early" via scheduler.triggerCheckForDueExecutions(). (See also enableImmediateExecution() on the Builder)

Versions / upgrading

See releases for release-notes.

Upgrading to 8.x

• Custom Schedules must implement a method boolean isDeterministic() to indicate whether they will always produce the same instants or not.

Upgrading to 4.x

• Add column consecutive_failures to the database schema. See table definitions for postgresql, oracle or mysql. null is handled as 0, so no need to update existing records.

Upgrading to 3.x

• No schema changes
• Task creation are preferrably done through builders in Tasks class

Upgrading to 2.x

• Add column task_data to the database schema. See table definitions for postgresql, oracle or mysql.

FAQ

Why db-scheduler when there is Quartz?

The goal of db-scheduler is to be non-invasive and simple to use, but still solve the persistence problem, and the cluster-coordination problem. It was originally targeted at applications with modest database schemas, to which adding 11 tables would feel a bit overkill..

Why use a RDBMS for persistence and coordination?

KISS. It's the most common type of shared state applications have.

I am missing feature X?

Please create an issue with the feature request and we can discuss it there. If you are impatient (or feel like contributing), pull requests are most welcome :)

Is anybody using it?

Yes. It is used in production at a number of companies, and have so far run smoothly.