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<blockquote>
<div></div></blockquote>
<div class="section" id="scheduler">
<span id="id1"></span><h1>Scheduler<a class="headerlink" href="#scheduler" title="Permalink to this headline"></a></h1>
<p>The Airflow scheduler monitors all tasks and DAGs, then triggers the
task instances once their dependencies are complete. Behind the scenes,
the scheduler spins up a subprocess, which monitors and stays in sync with all
DAGs in the specified DAG directory. Once per minute, by default, the scheduler
collects DAG parsing results and checks whether any active tasks can be triggered.</p>
<p>The Airflow scheduler is designed to run as a persistent service in an
Airflow production environment. To kick it off, all you need to do is
execute the <code class="docutils literal notranslate"><span class="pre">airflow</span> <span class="pre">scheduler</span></code> command. It uses the configuration specified in
<code class="docutils literal notranslate"><span class="pre">airflow.cfg</span></code>.</p>
<p>The scheduler uses the configured <a class="reference internal" href="../executor/index.html"><span class="doc">Executor</span></a> to run tasks that are ready.</p>
<p>To start a scheduler, simply run the command:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>airflow scheduler
</pre></div>
</div>
<p>Your DAGs will start executing once the scheduler is running successfully.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The first DAG Run is created based on the minimum <code class="docutils literal notranslate"><span class="pre">start_date</span></code> for the tasks in your DAG.
Subsequent DAG Runs are created by the scheduler process, based on your DAG’s <code class="docutils literal notranslate"><span class="pre">schedule_interval</span></code>,
sequentially.</p>
</div>
<p>The scheduler won't trigger your tasks until the period it covers has ended e.g., A job with <code class="docutils literal notranslate"><span class="pre">schedule_interval</span></code> set as <code class="docutils literal notranslate"><span class="pre">&#64;daily</span></code> runs after the day
has ended. This technique makes sure that whatever data is required for that period is fully available before the DAG is executed.
In the UI, it appears as if Airflow is running your tasks a day <strong>late</strong></p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If you run a DAG on a <code class="docutils literal notranslate"><span class="pre">schedule_interval</span></code> of one day, the run with data interval starting on <code class="docutils literal notranslate"><span class="pre">2019-11-21</span></code> triggers after <code class="docutils literal notranslate"><span class="pre">2019-11-21T23:59</span></code>.</p>
<p><strong>Let’s Repeat That</strong>, the scheduler runs your job one <code class="docutils literal notranslate"><span class="pre">schedule_interval</span></code> AFTER the start date, at the END of the interval.</p>
<p>You should refer to <a class="reference internal" href="../dag-run.html"><span class="doc">DAG Runs</span></a> for details on scheduling a DAG.</p>
</div>
<div class="section" id="dag-file-processing">
<h2>DAG File Processing<a class="headerlink" href="#dag-file-processing" title="Permalink to this headline"></a></h2>
<p>The Airflow Scheduler is responsible for turning the Python files contained in the DAGs folder into DAG objects that contain tasks to be scheduled.</p>
<p>There are two primary components involved in DAG file processing. The <code class="docutils literal notranslate"><span class="pre">DagFileProcessorManager</span></code> is a process executing an infinite loop that determines which files need
to be processed, and the <code class="docutils literal notranslate"><span class="pre">DagFileProcessorProcess</span></code> is a separate process that is started to convert an individual file into one or more DAG objects.</p>
<img alt="../_images/dag_file_processing_diagram.png" src="../_images/dag_file_processing_diagram.png" />
<p><code class="docutils literal notranslate"><span class="pre">DagFileProcessorManager</span></code> has the following steps:</p>
<ol class="arabic simple">
<li><p>Check for new files: If the elapsed time since the DAG was last refreshed is &gt; <a class="reference internal" href="../configurations-ref.html#config-scheduler-dag-dir-list-interval"><span class="std std-ref">dag_dir_list_interval</span></a> then update the file paths list</p></li>
<li><p>Exclude recently processed files: Exclude files that have been processed more recently than <a class="reference internal" href="../configurations-ref.html#config-scheduler-min-file-process-interval"><span class="std std-ref">min_file_process_interval</span></a> and have not been modified</p></li>
<li><p>Queue file paths: Add files discovered to the file path queue</p></li>
<li><p>Process files: Start a new <code class="docutils literal notranslate"><span class="pre">DagFileProcessorProcess</span></code> for each file, up to a maximum of <a class="reference internal" href="../configurations-ref.html#config-scheduler-parsing-processes"><span class="std std-ref">parsing_processes</span></a></p></li>
<li><p>Collect results: Collect the result from any finished DAG processors</p></li>
<li><p>Log statistics: Print statistics and emit <code class="docutils literal notranslate"><span class="pre">dag_processing.total_parse_time</span></code></p></li>
</ol>
<p><code class="docutils literal notranslate"><span class="pre">DagFileProcessorProcess</span></code> has the following steps:</p>
<ol class="arabic simple">
<li><p>Process file: The entire process must complete within <a class="reference internal" href="../configurations-ref.html#config-core-dag-file-processor-timeout"><span class="std std-ref">dag_file_processor_timeout</span></a></p></li>
<li><p>Load modules from file: Uses Python imp command, must complete within <a class="reference internal" href="../configurations-ref.html#config-core-dagbag-import-timeout"><span class="std std-ref">dagbag_import_timeout</span></a></p></li>
<li><p>Process modules: Find DAG objects within Python module</p></li>
<li><p>Return DagBag: Provide the <code class="docutils literal notranslate"><span class="pre">DagFileProcessorManager</span></code> a list of the discovered DAG objects</p></li>
</ol>
</div>
<div class="section" id="triggering-dag-with-future-date">
<h2>Triggering DAG with Future Date<a class="headerlink" href="#triggering-dag-with-future-date" title="Permalink to this headline"></a></h2>
<p>If you want to use 'external trigger' to run future-dated data intervals, set <code class="docutils literal notranslate"><span class="pre">allow_trigger_in_future</span> <span class="pre">=</span> <span class="pre">True</span></code> in <code class="docutils literal notranslate"><span class="pre">scheduler</span></code> section in <code class="docutils literal notranslate"><span class="pre">airflow.cfg</span></code>.
This only has effect if your DAG has no <code class="docutils literal notranslate"><span class="pre">schedule_interval</span></code>.
If you keep default <code class="docutils literal notranslate"><span class="pre">allow_trigger_in_future</span> <span class="pre">=</span> <span class="pre">False</span></code> and try 'external trigger' to run future-dated data intervals,
the scheduler won't execute it now but the scheduler will execute it in the future once the current date rolls over to the start of the data interval.</p>
</div>
<div class="section" id="running-more-than-one-scheduler">
<span id="scheduler-ha"></span><h2>Running More Than One Scheduler<a class="headerlink" href="#running-more-than-one-scheduler" title="Permalink to this headline"></a></h2>
<p>Airflow supports running more than one scheduler concurrently -- both for performance reasons and for
resiliency.</p>
<div class="section" id="overview">
<h3>Overview<a class="headerlink" href="#overview" title="Permalink to this headline"></a></h3>
<p>The <abbr title="highly available">HA</abbr> scheduler is designed to take advantage of the existing metadata database.
This was primarily done for operational simplicity: every component already has to speak to this DB, and by
not using direct communication or consensus algorithm between schedulers (Raft, Paxos, etc.) nor another
consensus tool (Apache Zookeeper, or Consul for instance) we have kept the &quot;operational surface area&quot; to a
minimum.</p>
<p>The scheduler now uses the serialized DAG representation to make its scheduling decisions and the rough
outline of the scheduling loop is:</p>
<ul class="simple">
<li><p>Check for any DAGs needing a new DagRun, and create them</p></li>
<li><p>Examine a batch of DagRuns for schedulable TaskInstances or complete DagRuns</p></li>
<li><p>Select schedulable TaskInstances, and whilst respecting Pool limits and other concurrency limits, enqueue
them for execution</p></li>
</ul>
<p>This does however place some requirements on the Database.</p>
</div>
<div class="section" id="database-requirements">
<span id="scheduler-ha-db-requirements"></span><h3>Database Requirements<a class="headerlink" href="#database-requirements" title="Permalink to this headline"></a></h3>
<p>The short version is that users of PostgreSQL 9.6+ or MySQL 8+ are all ready to go -- you can start running as
many copies of the scheduler as you like -- there is no further set up or config options needed. If you are
using a different database please read on.</p>
<p>To maintain performance and throughput there is one part of the scheduling loop that does a number of
calculations in memory (because having to round-trip to the DB for each TaskInstance would be too slow) so we
need to ensure that only a single scheduler is in this critical section at once - otherwise limits would not
be correctly respected. To achieve this we use database row-level locks (using <code class="docutils literal notranslate"><span class="pre">SELECT</span> <span class="pre">...</span> <span class="pre">FOR</span> <span class="pre">UPDATE</span></code>).</p>
<p>This critical section is where TaskInstances go from scheduled state and are enqueued to the executor, whilst
ensuring the various concurrency and pool limits are respected. The critical section is obtained by asking for
a row-level write lock on every row of the Pool table (roughly equivalent to <code class="docutils literal notranslate"><span class="pre">SELECT</span> <span class="pre">*</span> <span class="pre">FROM</span> <span class="pre">slot_pool</span> <span class="pre">FOR</span>
<span class="pre">UPDATE</span> <span class="pre">NOWAIT</span></code> but the exact query is slightly different).</p>
<p>The following databases are fully supported and provide an &quot;optimal&quot; experience:</p>
<ul class="simple">
<li><p>PostgreSQL 9.6+</p></li>
<li><p>MySQL 8+</p></li>
</ul>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>MariaDB did not implement the <code class="docutils literal notranslate"><span class="pre">SKIP</span> <span class="pre">LOCKED</span></code> or <code class="docutils literal notranslate"><span class="pre">NOWAIT</span></code> SQL clauses until version
<a class="reference external" href="https://jira.mariadb.org/browse/MDEV-25433">10.6.0</a>.
Without these features, running multiple schedulers is not supported and deadlock errors have been reported. MariaDB
10.6.0 and following may work appropriately with multiple schedulers, but this has not been tested.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>MySQL 5.x does not support <code class="docutils literal notranslate"><span class="pre">SKIP</span> <span class="pre">LOCKED</span></code> or <code class="docutils literal notranslate"><span class="pre">NOWAIT</span></code>, and additionally is more prone to deciding
queries are deadlocked, so running with more than a single scheduler on MySQL 5.x is not supported or
recommended.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Microsoft SQLServer has not been tested with HA.</p>
</div>
</div>
</div>
<div class="section" id="fine-tuning-your-scheduler-performance">
<h2>Fine-tuning your Scheduler performance<a class="headerlink" href="#fine-tuning-your-scheduler-performance" title="Permalink to this headline"></a></h2>
<div class="section" id="what-impacts-scheduler-s-performance">
<h3>What impacts scheduler's performance<a class="headerlink" href="#what-impacts-scheduler-s-performance" title="Permalink to this headline"></a></h3>
<p>The Scheduler is responsible for two operations:</p>
<ul class="simple">
<li><p>continuously parsing DAG files and synchronizing with the DAG in the database</p></li>
<li><p>continuously scheduling tasks for execution</p></li>
</ul>
<p>Those two tasks are executed in parallel by the scheduler and run independently of each other in
different processes. In order to fine-tune your scheduler, you need to include a number of factors:</p>
<ul class="simple">
<li><dl class="simple">
<dt>The kind of deployment you have</dt><dd><ul>
<li><p>what kind of filesystem you have to share the DAGs (impacts performance of continuously reading DAGs)</p></li>
<li><p>how fast the filesystem is (in many cases of distributed cloud filesystem you can pay extra to get
more throughput/faster filesystem</p></li>
<li><p>how much memory you have for your processing</p></li>
<li><p>how much CPU you have available</p></li>
<li><p>how much networking throughput you have available</p></li>
</ul>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>The logic and definition of your DAG structure:</dt><dd><ul>
<li><p>how many DAG files you have</p></li>
<li><p>how many DAGs you have in your files</p></li>
<li><p>how large the DAG files are (remember dag parser needs to read and parse the file every n seconds)</p></li>
<li><p>how complex they are (i.e. how fast they can be parsed, how many tasks and dependencies they have)</p></li>
<li><p>whether parsing your DAG file involves importing a lot of libraries or heavy processing at the top level
(Hint! It should not. See <a class="reference internal" href="../best-practices.html#best-practices-top-level-code"><span class="std std-ref">Top level Python Code</span></a>)</p></li>
</ul>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>The scheduler configuration</dt><dd><ul>
<li><p>How many schedulers you have</p></li>
<li><p>How many parsing processes you have in your scheduler</p></li>
<li><p>How much time scheduler waits between re-parsing of the same DAG (it happens continuously)</p></li>
<li><p>How many task instances scheduler processes in one loop</p></li>
<li><p>How many new DAG runs should be created/scheduled per loop</p></li>
<li><p>How often the scheduler should perform cleanup and check for orphaned tasks/adopting them</p></li>
</ul>
</dd>
</dl>
</li>
</ul>
<p>In order to perform fine-tuning, it's good to understand how Scheduler works under-the-hood.
You can take a look at the Airflow Summit 2021 talk
<a class="reference external" href="https://youtu.be/DYC4-xElccE">Deep Dive into the Airflow Scheduler talk</a> to perform the fine-tuning.</p>
</div>
<div class="section" id="how-to-approach-scheduler-s-fine-tuning">
<h3>How to approach Scheduler's fine-tuning<a class="headerlink" href="#how-to-approach-scheduler-s-fine-tuning" title="Permalink to this headline"></a></h3>
<p>Airflow gives you a lot of &quot;knobs&quot; to turn to fine tune the performance but it's a separate task,
depending on your particular deployment, your DAG structure, hardware availability and expectations,
to decide which knobs to turn to get best effect for you. Part of the job when managing the
deployment is to decide what you are going to optimize for. Some users are ok with
30 seconds delays of new DAG parsing, at the expense of lower CPU usage, whereas some other users
expect the DAGs to be parsed almost instantly when they appear in the DAGs folder at the
expense of higher CPU usage for example.</p>
<p>Airflow gives you the flexibility to decide, but you should find out what aspect of performance is
most important for you and decide which knobs you want to turn in which direction.</p>
<p>Generally for fine-tuning, your approach should be the same as for any performance improvement and
optimizations (we will not recommend any specific tools - just use the tools that you usually use
to observe and monitor your systems):</p>
<ul class="simple">
<li><p>its extremely important to monitor your system with the right set of tools that you usually use to
monitor your system. This document does not go into details of particular metrics and tools that you
can use, it just describes what kind of resources you should monitor, but you should follow your best
practices for monitoring to grab the right data.</p></li>
<li><p>decide which aspect of performance is most important for you (what you want to improve)</p></li>
<li><p>observe your system to see where your bottlenecks are: CPU, memory, I/O are the usual limiting factors</p></li>
<li><p>based on your expectations and observations - decide what is your next improvement and go back to
the observation of your performance, bottlenecks. Performance improvement is an iterative process.</p></li>
</ul>
</div>
<div class="section" id="what-resources-might-limit-scheduler-s-performance">
<h3>What resources might limit Scheduler's performance<a class="headerlink" href="#what-resources-might-limit-scheduler-s-performance" title="Permalink to this headline"></a></h3>
<p>There are several areas of resource usage that you should pay attention to:</p>
<ul class="simple">
<li><p>FileSystem performance. Airflow Scheduler relies heavily on parsing (sometimes a lot) of Python
files, which are often located on a shared filesystem. Airflow Scheduler continuously reads and
re-parses those files. The same files have to be made available to workers, so often they are
stored in a distributed filesystem. You can use various filesystems for that purpose (NFS, CIFS, EFS,
GCS fuse, Azure File System are good examples). There are various parameters you can control for those
filesystems and fine-tune their performance, but this is beyond the scope of this document. You should
observe statistics and usage of your filesystem to determine if problems come from the filesystem
performance. For example there are anecdotal evidences that increasing IOPS (and paying more) for the
EFS performance, dramatically improves stability and speed of parsing Airflow DAGs when EFS is used.</p></li>
<li><p>Another solution to FileSystem performance, if it becomes your bottleneck, is to turn to alternative
mechanisms of distributing your DAGs. Embedding DAGs in your image and GitSync distribution have both
the property that the files are available locally for Scheduler and it does not have to use a
distributed filesystem to read the files, the files are available locally for the Scheduler and it is
usually as fast as it can be, especially if your machines use fast SSD disks for local storage. Those
distribution mechanisms have other characteristics that might make them not the best choice for you,
but if your problems with performance come from distributed filesystem performance, they might be the
best approach to follow.</p></li>
<li><p>Database connections and Database usage might become a problem as you want to increase performance and
process more things in parallel. Airflow is known from being &quot;database-connection hungry&quot; - the more DAGs
you have and the more you want to process in parallel, the more database connections will be opened.
This is generally not a problem for MySQL as its model of handling connections is thread-based, but this
might be a problem for Postgres, where connection handling is process-based. It is a general consensus
that if you have even medium size Postgres-based Airflow installation, the best solution is to use
<a class="reference external" href="https://www.pgbouncer.org/">PGBouncer</a> as a proxy to your database. The <a class="reference external" href="/docs/helm-chart/stable/index.html" title="(in helm-chart v1.6.0-dev)"><span>Helm Chart for Apache Airflow</span></a>
supports PGBouncer out-of-the-box. For MsSQL we have not yet worked out the best practices as support
for MsSQL is still experimental.</p></li>
<li><p>CPU usage is most important for FileProcessors - those are the processes that parse and execute
Python DAG files. Since Schedulers triggers such parsing continuously, when you have a lot of DAGs,
the processing might take a lot of CPU. You can mitigate it by decreasing the
<a class="reference internal" href="../configurations-ref.html#config-scheduler-min-file-process-interval"><span class="std std-ref">min_file_process_interval</span></a>, but this is one of the mentioned trade-offs,
result of this is that changes to such files will be picked up slower and you will see delays between
submitting the files and getting them available in Airflow UI and executed by Scheduler. Optimizing
the way how your DAGs are built, avoiding external data sources is your best approach to improve CPU
usage. If you have more CPUs available, you can increase number of processing threads
<a class="reference internal" href="../configurations-ref.html#config-scheduler-parsing-processes"><span class="std std-ref">parsing_processes</span></a>, Also Airflow Scheduler scales almost linearly with
several instances, so you can also add more Schedulers if your Scheduler's performance is CPU-bound.</p></li>
<li><p>Airflow might use quite significant amount of memory when you try to get more performance out of it.
Often more performance is achieved in Airflow by increasing number of processes handling the load,
and each process requires whole interpreter of Python loaded, a lot of classes imported, temporary
in-memory storage. A lot of it is optimized by Airflow by using forking and copy-on-write memory used
but in case new classes are imported after forking this can lead to extra memory pressure.
You need to observe if your system is using more memory than it has - which results with using swap disk,
which dramatically decreases performance. Note that Airflow Scheduler in versions prior to <code class="docutils literal notranslate"><span class="pre">2.1.4</span></code>
generated a lot of <code class="docutils literal notranslate"><span class="pre">Page</span> <span class="pre">Cache</span></code> memory used by log files (when the log files were not removed).
This was generally harmless, as the memory is just cache and could be reclaimed at any time by the system,
however in version <code class="docutils literal notranslate"><span class="pre">2.1.4</span></code> and beyond, writing logs will not generate excessive <code class="docutils literal notranslate"><span class="pre">Page</span> <span class="pre">Cache</span></code> memory.
Regardless - make sure when you look at memory usage, pay attention to the kind of memory you are observing.
Usually you should look at <code class="docutils literal notranslate"><span class="pre">working</span> <span class="pre">memory``(names</span> <span class="pre">might</span> <span class="pre">vary</span> <span class="pre">depending</span> <span class="pre">on</span> <span class="pre">your</span> <span class="pre">deployment)</span> <span class="pre">rather</span>
<span class="pre">than</span> <span class="pre">``total</span> <span class="pre">memory</span> <span class="pre">used</span></code>.</p></li>
</ul>
</div>
<div class="section" id="what-can-you-do-to-improve-scheduler-s-performance">
<h3>What can you do, to improve Scheduler's performance<a class="headerlink" href="#what-can-you-do-to-improve-scheduler-s-performance" title="Permalink to this headline"></a></h3>
<p>When you know what your resource usage is, the improvements that you can consider might be:</p>
<ul class="simple">
<li><p>improve the logic, efficiency of parsing and reduce complexity of your top-level DAG Python code. It is
parsed continuously so optimizing that code might bring tremendous improvements, especially if you try
to reach out to some external databases etc. while parsing DAGs (this should be avoided at all cost).
The <a class="reference internal" href="../best-practices.html#best-practices-top-level-code"><span class="std std-ref">Top level Python Code</span></a> explains what are the best practices for writing your top-level
Python code. The <a class="reference internal" href="../best-practices.html#best-practices-reducing-dag-complexity"><span class="std std-ref">Reducing DAG complexity</span></a> document provides some ares that you might
look at when you want to reduce complexity of your code.</p></li>
<li><p>improve utilization of your resources. This is when you have a free capacity in your system that
seems underutilized (again CPU, memory I/O, networking are the prime candidates) - you can take
actions like increasing number of schedulers, parsing processes or decreasing intervals for more
frequent actions might bring improvements in performance at the expense of higher utilization of those.</p></li>
<li><p>increase hardware capacity (for example if you see that CPU is limiting you or that I/O you use for
DAG filesystem is at its limits). Often the problem with scheduler performance is
simply because your system is not &quot;capable&quot; enough and this might be the only way. For example if
you see that you are using all CPU you have on machine, you might want to add another scheduler on
a new machine - in most cases, when you add 2nd or 3rd scheduler, the capacity of scheduling grows
linearly (unless the shared database or filesystem is a bottleneck).</p></li>
<li><p>experiment with different values for the &quot;scheduler tunables&quot;. Often you might get better effects by
simply exchanging one performance aspect for another. For example if you want to decrease the
CPU usage, you might increase file processing interval (but the result will be that new DAGs will
appear with bigger delay). Usually performance tuning is the art of balancing different aspects.</p></li>
<li><p>sometimes you change scheduler behaviour slightly (for example change parsing sort order)
in order to get better fine-tuned results for your particular deployment.</p></li>
</ul>
</div>
<div class="section" id="scheduler-configuration-options">
<span id="scheduler-ha-tunables"></span><h3>Scheduler Configuration options<a class="headerlink" href="#scheduler-configuration-options" title="Permalink to this headline"></a></h3>
<p>The following config settings can be used to control aspects of the Scheduler.
However you can also look at other non-performance-related scheduler configuration parameters available at
<a class="reference internal" href="../configurations-ref.html"><span class="doc">Configuration Reference</span></a> in <code class="docutils literal notranslate"><span class="pre">[scheduler]</span></code> section.</p>
<ul>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-max-dagruns-to-create-per-loop"><span class="std std-ref">max_dagruns_to_create_per_loop</span></a></p>
<p>This changes the number of DAGs that are locked by each scheduler when
creating DAG runs. One possible reason for setting this lower is if you
have huge DAGs (in the order of 10k+ tasks per DAG) and are running multiple schedulers, you won't want one
scheduler to do all the work.</p>
</li>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-max-dagruns-per-loop-to-schedule"><span class="std std-ref">max_dagruns_per_loop_to_schedule</span></a></p>
<p>How many DagRuns should a scheduler examine (and lock) when scheduling
and queuing tasks. Increasing this limit will allow more throughput for
smaller DAGs but will likely slow down throughput for larger (&gt;500
tasks for example) DAGs. Setting this too high when using multiple
schedulers could also lead to one scheduler taking all the DAG runs
leaving no work for the others.</p>
</li>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-use-row-level-locking"><span class="std std-ref">use_row_level_locking</span></a></p>
<p>Should the scheduler issue <code class="docutils literal notranslate"><span class="pre">SELECT</span> <span class="pre">...</span> <span class="pre">FOR</span> <span class="pre">UPDATE</span></code> in relevant queries.
If this is set to False then you should not run more than a single
scheduler at once.</p>
</li>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-pool-metrics-interval"><span class="std std-ref">pool_metrics_interval</span></a></p>
<p>How often (in seconds) should pool usage stats be sent to statsd (if
statsd_on is enabled). This is a <em>relatively</em> expensive query to compute
this, so this should be set to match the same period as your statsd roll-up
period.</p>
</li>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-orphaned-tasks-check-interval"><span class="std std-ref">orphaned_tasks_check_interval</span></a></p>
<p>How often (in seconds) should the scheduler check for orphaned tasks or dead
SchedulerJobs.</p>
<p>This setting controls how a dead scheduler will be noticed and the tasks it
was &quot;supervising&quot; get picked up by another scheduler. The tasks will stay
running, so there is no harm in not detecting this for a while.</p>
<p>When a SchedulerJob is detected as &quot;dead&quot; (as determined by
<a class="reference internal" href="../configurations-ref.html#config-scheduler-scheduler-health-check-threshold"><span class="std std-ref">scheduler_health_check_threshold</span></a>) any running or
queued tasks that were launched by the dead process will be &quot;adopted&quot; and
monitored by this scheduler instead.</p>
</li>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-dag-dir-list-interval"><span class="std std-ref">dag_dir_list_interval</span></a>
How often (in seconds) to scan the DAGs directory for new files.</p></li>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-file-parsing-sort-mode"><span class="std std-ref">file_parsing_sort_mode</span></a>
The scheduler will list and sort the DAG files to decide the parsing order.</p></li>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-max-tis-per-query"><span class="std std-ref">max_tis_per_query</span></a>
The batch size of queries in the scheduling main loop. If this is too high, SQL query
performance may be impacted by complexity of query predicate, and/or excessive locking.</p>
<p>Additionally, you may hit the maximum allowable query length for your db.
Set this to 0 for no limit (not advised).</p>
</li>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-min-file-process-interval"><span class="std std-ref">min_file_process_interval</span></a>
Number of seconds after which a DAG file is re-parsed. The DAG file is parsed every
min_file_process_interval number of seconds. Updates to DAGs are reflected after
this interval. Keeping this number low will increase CPU usage.</p></li>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-parsing-processes"><span class="std std-ref">parsing_processes</span></a>
The scheduler can run multiple processes in parallel to parse DAG files. This defines
how many processes will run.</p></li>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-scheduler-idle-sleep-time"><span class="std std-ref">scheduler_idle_sleep_time</span></a>
Controls how long the scheduler will sleep between loops, but if there was nothing to do
in the loop. i.e. if it scheduled something then it will start the next loop
iteration straight away. This parameter is badly named (historical reasons) and it will be
renamed in the future with deprecation of the current name.</p></li>
<li><p><a class="reference internal" href="../configurations-ref.html#config-scheduler-schedule-after-task-execution"><span class="std std-ref">schedule_after_task_execution</span></a>
Should the Task supervisor process perform a “mini scheduler” to attempt to schedule more tasks of
the same DAG. Leaving this on will mean tasks in the same DAG execute quicker,
but might starve out other DAGs in some circumstances.</p></li>
</ul>
</div>
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<li><a class="reference internal" href="#dag-file-processing">DAG File Processing</a></li>
<li><a class="reference internal" href="#triggering-dag-with-future-date">Triggering DAG with Future Date</a></li>
<li><a class="reference internal" href="#running-more-than-one-scheduler">Running More Than One Scheduler</a><ul>
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<li><a class="reference internal" href="#how-to-approach-scheduler-s-fine-tuning">How to approach Scheduler's fine-tuning</a></li>
<li><a class="reference internal" href="#what-resources-might-limit-scheduler-s-performance">What resources might limit Scheduler's performance</a></li>
<li><a class="reference internal" href="#what-can-you-do-to-improve-scheduler-s-performance">What can you do, to improve Scheduler's performance</a></li>
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