It can be created by the scheduler (for regular runs) or by an external trigger. WebScheduling & Triggers. Apache Airflow, Apache, Airflow, the Airflow logo, and the Apache feather logo are either registered trademarks or trademarks of The Apache Software Foundation. instead of SSHHook. It also solves the discovery problem that arises as your infrastructure grows. Last but not least, a DAG is a data pipeline in Apache Airflow. can use to prove its identity when making calls to Google APIs or third-party services. If a field is marked as being templated and is mapped, it will not be templated. For a multi-node setup, you should Some instructions below: Read the airflow official XCom docs. Those two containers should share The python modules in the plugins folder get imported, and macros and web views Airflow tries to be smart and coerce the value automatically, but will emit a warning for this so you are aware of this. As part of our efforts to make the Scheduler more performant and reliable, we have changed this behavior to log the exception instead. This produces two task instances at run-time printing 1 and 2 respectively. if started by systemd. access only to short-lived credentials. Using Airflow If you need access to other service accounts, you can you want to plug into Airflow. This component is responsible for scheduling jobs. WebAn Airflow DAG defined with a start_date, possibly an end_date, and a non-dataset schedule, defines a series of intervals which the scheduler turns into individual DAG runs and executes. Please official Helm chart for Airflow that helps you define, install, and upgrade deployment. in production can lead to data loss in multiple scenarios. the Admin->Configuration menu. secrets backend. only run task instances sequentially. WebThis is similar to defining your tasks in a for loop, but instead of having the DAG file fetch the data and do that itself, the scheduler can do this based on the output of a previous task. This is one of the most important characteristics of good ETL architectures. a volume where the temporary token should be written by the airflow kerberos and read by the workers. WebThere are a couple of things to note: The callable argument of map() (create_copy_kwargs in the example) must not be a task, but a plain Python function. You can accomplish this using the format AIRFLOW__{SECTION}__{KEY}. # Airflow needs a home. Behind the scenes, the scheduler spins up a subprocess, which monitors and stays in sync with all DAGs in the specified DAG directory. such as PostgreSQL or MySQL. interpreter and re-parse all of the Airflow code and start up routines this is a big benefit for shorter Apache Airflow v2. Assigning multiple parameters to a non-TaskFlow operator. {operators,sensors,hooks}.
is no longer supported, and these extensions should If you want to create a DOT file then you should execute the following command: airflow dags test save-dagrun output.dot constraint files to enable reproducible installation, so using pip and constraint files is recommended. list(values) will give you a real list, but since this would eagerly load values from all of the referenced upstream mapped tasks, you must be aware of the potential performance implications if the mapped number is large. This allows the user to run Airflow without any external In its simplest form you can map over a list defined directly in your DAG file using the expand() function instead of calling your task directly. This function is called for each item in the iterable used for task-mapping, similar to how Pythons built-in map() works. The Helm provides a simple mechanism to deploy software to a Kubernetes cluster. These pipelines are acyclic since they need a point of completion. The grid view also provides visibility into your mapped tasks in the details panel: Only keyword arguments are allowed to be passed to expand(). organizations have different stacks and different needs. It is an extremely robust way to manage Linux access properly as it stores Successful installation requires a Python 3 environment. If you want to run production-grade Airflow, Airflow python data pipeline Airflow DAGDirected acyclic graph , HivePrestoMySQLHDFSPostgres hook Web , A B , Airflow DAG ()DAG task DAG task DAG , Airflow crontab python datatime datatime delta , $AIRFLOW_HOME dags dag , python $AIRFLOW_HOME/dags/demo.py , airflow list_dags -sd $AIRFLOW_HOME/dags dags, # airflow test dag_id task_id execution_time, # webserver, 8080`-p`, Scheduler DAG , Executor LocalExecutor CeleryExecutor . You should use the Listeners can register to, # listen to particular events that happen in Airflow, like. # Copy files to another bucket, based on the file's extension. ; Be sure to understand the documentation of pythonOperator. definitions in Airflow. Since it is common to want to transform the output data format for task mapping, especially from a non-TaskFlow operator, where the output format is pre-determined and cannot be easily converted (such as create_copy_kwargs in the above example), a special map() function can be used to easily perform this kind of transformation. For example, if you want to download files from S3, but rename those files, something like this would be possible: The zip function takes arbitrary positional arguments, and return an iterable of tuples of the positional arguments count. Airflow Scheduler Parameters for DAG Runs. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. It is time to deploy your DAG in production. See example below. # Skip files not ending with these suffixes. copy_files), not a standalone task in the DAG. Note that returning None does not work here. Heres what the class you need to derive Web Identity Federation, We strongly suggest that you should protect all your views with CSRF. LocalExecutor for a single machine. There are three basic kinds of Task: Operators, predefined task templates that you can string together quickly to build most parts of your DAGs. Each of the vertices has a particular direction that shows the relationship between certain nodes. WebA DAG has no cycles, never. Different Tasks are defined based on the abstraction of Operators (see Airflow docs here) which represent a single idempotent task. It uses the pre-configured Last but not least, when a DAG is triggered, a DAGRun is created. # A list of Listeners that plugin provides. e.g. and offers the nsswitch user lookup into the metadata service as well. Apache Airflow, Apache, Airflow, the Airflow logo, and the Apache feather logo are either registered trademarks or trademarks of The Apache Software Foundation. For instance, you cant have the upstream task return a plain string it must be a list or a dict. Different organizations have different stacks and different needs. Once that is done, you can run -. defined as class attributes, but you can also define them as properties if you need to perform WebAirflow consist of several components: Workers - Execute the assigned tasks. Secured Server and Service Access on Google Cloud. If you want to run the individual parts of Airflow manually rather than using Airflow is a Workflow engine which means: It is highly versatile and can be used across many many domains: The vertices and edges (the arrows linking the nodes) have an order and direction associated to them. WebThe Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. A Snowflake Account. access to the Keytab file (preferably configured as secret resource). All other products or name brands are trademarks of their respective holders, including The Apache Software Foundation. Google Cloud, the identity is provided by Airflow Scheduler Scheduler DAG Scheduler Worker Specific map index or map indexes to pull, or None if we Powered by, 'Whatever you return gets printed in the logs', Airflow 101: working locally and familiarise with the tool, Manage scheduling and running jobs and data pipelines, Ensures jobs are ordered correctly based on dependencies, Manage the allocation of scarce resources, Provides mechanisms for tracking the state of jobs and recovering from failure, Created at Spotify (named after the plumber), Python open source projects for data pipelines, Integrate with a number of sources (databases, filesystems), Ability to identify the dependencies and execution, Scheduler support: Airflow has built-in support using schedulers, Scalability: Airflow has had stability issues in the past. will automatically load the registered plugins from the entrypoint list. you to get up and running quickly and take a tour of the UI and the Web server - HTTP Server provides access to DAG/task status information. impersonate other service accounts to exchange the token with The big functional elements are listed below: Scheduler HA - Improve Scheduler performance and reliability ; Airflow REST API ; Functional DAGs ; Production-ready Docker Image (For scheduled runs, the default values are used.) Amazon CloudWatch. plugins can be a way for companies to customize their Airflow installation the results are reproducible). Webhow to use an opensource tool like Airflow to create a data scheduler; how do we write a DAG and upload it onto Airflow; how to build scalable pipelines using dbt, Airflow and Snowflake; What You'll Need. To protect your organizations data, every request you make should contain sender identity. The logs only appear in your DFS after the task has finished. WebHooks act as an interface to communicate with the external shared resources in a DAG. # Collect the transformed inputs, expand the operator to load each one of them to the target. does not send any dag files or configuration. The other The task state is retrieved and updated from the database accordingly. ; be sure to understand: context becomes available only when Operator is actually executed, not during DAG-definition. Thus, the account keys are still managed by Google You can override defaults using environment variables, see Configuration Reference. When using apache-airflow >= 2.0.0, DAG Serialization is enabled by default, hence Webserver does not need access to DAG files, so git-sync sidecar is not run on Webserver. To create a plugin you will need to derive the For example, you can use the web interface to review the progress of a DAG, set up a new data connection, or review logs from previous DAG runs. WebAirflow Airflow Airflow python data pipeline Airflow DAGDirected acyclic graph you can exchange the Google Cloud Platform identity to the Amazon Web Service identity, This way, the logs are available even after the node goes down or gets replaced. the all-in-one standalone command, you can instead run: From this point, you can head to the Tutorials section for further examples or the How-to Guides section if youre ready to get your hands dirty. | Task are defined bydag_id defined by user name | Task are defined by task name and parameters | Some configurations such as the Airflow Backend connection URI can be derived from bash commands as well: Airflow users occasionally report instances of the scheduler hanging without a trace, for example in these issues: To mitigate these issues, make sure you have a health check set up that will detect when your scheduler has not heartbeat in a while. You should Following a bumpy launch week that saw frequent server trouble and bloated player queues, Blizzard has announced that over 25 million Overwatch 2 players have logged on in its first 10 days. different flavors of data and metadata. Only keyword arguments are allowed to be passed to partial(). # Expand the operator to transform each input. For example, this will print {{ ds }} and not a date stamp: If you want to interpolate values either call task.render_template yourself, or use interpolation: There are two limits that you can place on a task: the number of mapped task instances can be created as the result of expansion. Only the Kerberos side-car has access to !function (d, s, id) { var js, fjs = d.getElementsByTagName(s)[0], p = /^http:/.test(d.location) ? the Celery executor. While this is very limiting, it allows WebException from DAG callbacks used to crash the Airflow Scheduler. code you will need to restart those processes. Limiting parallel copies of a mapped task. When we say that something is idempotent it means it will produce the same result regardless of how many times this is run (i.e. Airflow uses SequentialExecutor by default. Webairflow-scheduler - The scheduler monitors all tasks and DAGs, ./dags - you can put your DAG files here../logs - contains logs from task execution and scheduler../plugins - you can put your custom plugins here. Tells the scheduler to create a DAG run to "catch up" to the specific time interval in catchup_by_default. WebYou can see the .airflowignore file at the root of your folder. Please note name inside this class must be specified. The web server is a part of Cloud Composer environment architecture. Create an empty DB and give airflows user the permission to CREATE/ALTER it. Scheduler - Responsible for adding the necessary tasks to the queue. the default identity to another service account. See Logging for Tasks for configurations. Airflow version Airflow configuration option scheduler.catchup_by_default. To enable automatic reloading of the webserver, when changes in a directory with plugins has been detected, nature, the user is limited to executing at most one task at a time. command line utilities. This is similar to defining your tasks in a for loop, but instead of having the DAG file fetch the data and do that itself, the scheduler can do this based on the output of a previous task. To do this, first, you need to make sure that the Airflow to reflect their ecosystem. For each DAG Run, this parameter is returned by the DAGs timetable. Values passed from the mapped task is a lazy proxy. WebIf you want to create a PNG file then you should execute the following command: airflow dags test save-dagrun output.png. This command dumps information about loaded plugins. For example, if we want to only copy files from an S3 bucket to another with certain extensions, we could implement create_copy_kwargs like this instead: This makes copy_files only expand against .json and .yml files, while ignoring the rest. can stand on their own and do not need to share resources among them). loaded/parsed in any long-running Airflow process.). As well as a single parameter it is possible to pass multiple parameters to expand. running tasks. Heres a list of DAG run parameters that youll be dealing with when creating/running your own DAG runs: data_interval_start: A datetime object that specifies the start date and time of the data interval. Node B could be the code for checking that there are no duplicate records, and so on. airflow. additional initialization. If the package is installed, Airflow For more information on setting the configuration, see Setting Configuration Options. The big functional elements are listed below: Scheduler HA - Improve Scheduler performance and reliability ; Airflow REST API ; Functional DAGs ; Production-ready Docker Image This is especially useful for conditional logic in task mapping. WebYou should be able to see the status of the jobs change in the example_bash_operator DAG as you run the commands below. You will need the following things before beginning: Snowflake . | Airflow | Luigi | Neither the entrypoint name (eg, my_plugin) nor the name of the Therefore it will post a message on a message bus, or insert it into a database (depending of the backend) This status is used by the scheduler to update the state of the task The use of a database is highly recommended When not specified, Azure Blobstorage). SequentialExecutor which will By default, the zipped iterables length is the same as the shortest of the zipped iterables, with superfluous items dropped. It provides cryptographic credentials that your workload To do this, you can use the expand_kwargs function, which takes a sequence of mappings to map against. key is always held in escrow and is never directly accessible. This will show Total was 9 in the task logs when executed. they should land, alert people, and expose visualizations of outages. For more information about service accounts in the Airflow, see Google Cloud Connection. separately. them to appropriate format and workflow that your tool requires. schedule (ScheduleArg) Defines the rules according to which DAG runs are scheduled.Can accept cron string, And it makes sense because in taxonomy database. But if needed, you can exclude Thus your workflows become more explicit and maintainable (atomic tasks). fairly quickly since no parallelization is possible using this database All other products or name brands are trademarks of their respective holders, including The Apache Software Foundation. Sequential Executor also pauses Workload Identity to assign | Task code to the worker | Workers started by Python file where the tasks are defined | the one for every workday, run An optional keyword argument default can be passed to switch the behavior to match Pythons itertools.zip_longestthe zipped iterable will have the same length as the longest of the zipped iterables, with missing items filled with the value provided by default. The best practice to implement proper security mechanism in this case is to make sure that worker # The Standalone command will initialise the database, make a user, # Visit localhost:8080 in the browser and use the admin account details, # Enable the example_bash_operator dag in the home page. Max Active Tasks Per DAG. instance name instead of the network address. The code below defines a plugin that injects a set of dummy object We provide a Docker Image (OCI) for Apache Airflow for use in a containerized environment. It is also to want to combine multiple input sources into one task mapping iterable. ), and then the consumer task will be called four times, once with each value in the return of make_list. We have effectively finalized the scope of Airflow 2.0 and now actively workings towards merging all the code and getting it released. If an upstream task returns an unmappable type, the mapped task will fail at run-time with an UnmappableXComTypePushed exception. You can use the Flask CLI to troubleshoot problems. WebDAGs. # NOTE: Ensure your plugin has *args, and **kwargs in the method definition, # to protect against extra parameters injected into the on_load(), # A list of global operator extra links that can redirect users to, # external systems. Airflow comes with an SQLite backend by default. 'http' : 'https'; if (!d.getElementById(id)) { js = d.createElement(s); js.id = id; js.src = p + '://platform.twitter.com/widgets.js'; fjs.parentNode.insertBefore(js, fjs); } }(document, 'script', 'twitter-wjs'); 2019, Tania Allard. which effectively means access to Amazon Web Service platform. Apache Airflow, Apache, Airflow, the Airflow logo, and the Apache feather logo are either registered trademarks or trademarks of The Apache Software Foundation. It should contain either regular expressions (the default) or glob expressions for the paths that should be ignored. ComputeEngineHook Sometimes an upstream needs to specify multiple arguments to a downstream operator. WebDAG: Directed acyclic graph, a set of tasks with explicit execution order, beginning, and end; DAG run: individual execution/run of a DAG; Debunking the DAG. WebWhen Airflows scheduler encounters a DAG, it calls one of the two methods to know when to schedule the DAGs next run. Lets see what precautions you need to take. If the user-supplied values dont pass validation, Airflow shows a warning instead of creating the dagrun. If you are using disposable nodes in your cluster, configure the log storage to be a distributed file system Each request for refresh uses a configured principal, and only keytab valid for the principal specified just be imported as regular python modules. The ComputeEngineHook support authorization with Even with the use of the backend secret, the service account key is available for Airflow is a platform that lets you build and run workflows.A workflow is represented as a DAG (a Directed Acyclic Graph), and contains individual pieces of work called Tasks, arranged with dependencies and data flows taken into account.. A DAG specifies the dependencies between Tasks, and the order in which to execute them Each Cloud Composer environment has a web server that runs the Airflow web interface. features to its core by simply dropping files in your Instead of creating a connection per task, you can retrieve a connection from the hook and utilize it. Airflow uses | Centralized scheduler (Celery spins up workers) | Centralized scheduler in charge of deduplication sending tasks (Tornado based) |, a.k.a an introduction to all things DAGS and pipelines joy. Once you have configured the executor, it is necessary to make sure that every node in the cluster contains Web server - HTTP Server provides access to DAG/task status information. If you want to map over the result of a classic operator, you should explicitly reference the output, instead of the operator itself. The Helm Chart uses official Docker image and Dockerfile that is also maintained and released by the community. Here are a few commands that will trigger a few task instances. Some arguments are not mappable and must be passed to partial(), such as task_id, queue, pool, and most other arguments to BaseOperator. This is also useful for passing things such as connection IDs, database table names, or bucket names to tasks. WebThe scheduler pod will sync DAGs from a git repository onto the PVC every configured number of seconds. {operators,sensors,hooks}., core.execute_tasks_new_python_interpreter, # A list of class(es) derived from BaseHook, # A list of references to inject into the macros namespace, # A list of Blueprint object created from flask.Blueprint. Scheduler - Responsible for adding the necessary tasks to the queue. You can use the Kerberos Keytab to authenticate in the KDC to obtain a valid token, and then refreshing valid token In the Kubernetes environment, this can be realized by the concept of side-car, where both Kerberos Please note that the queue at The above example can therefore be modified like this: The callable argument of map() (create_copy_kwargs in the example) must not be a task, but a plain Python function. 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Task instances at run-time with an UnmappableXComTypePushed exception instructions below: Read the official! The task state is retrieved and updated from the mapped task will be four! Is returned by the community Helm provides a simple mechanism to deploy software to a Kubernetes cluster accessible... All the code for checking that there are no duplicate records, then. Making calls to dag scheduler airflow APIs or third-party services can exclude thus your workflows become more explicit and maintainable ( tasks... Operators ( see Airflow docs here ) which represent a single idempotent task the following before! Shows the relationship between certain nodes tool requires # Collect the transformed inputs, expand the operator to load one. Airflow that helps you define, install, and so on for the paths should. Return of make_list called four times, once with each value in the return of make_list accounts you. Class must be specified are trademarks of their respective holders, including the Apache software Foundation regular (... You run the commands below this is a data pipeline in Apache Airflow v2 configured number of seconds Airflow.. That should be ignored always held in escrow and is never directly accessible all your views with CSRF or... The other the task has finished, expand the operator to load each one of them to the time. As an interface to communicate with the external shared resources in a DAG dag scheduler airflow. Cloud Connection # listen to particular events that happen in Airflow, like by. Big benefit for shorter Apache Airflow on the abstraction of Operators ( see Airflow here. Airflow docs here ) which represent a single parameter it is an extremely robust way to manage access... Tasks are defined based on the file 's extension Web identity Federation, we strongly suggest that you use! Third-Party services operator is actually executed, not during DAG-definition are acyclic since they need a of. Fail at run-time printing 1 and 2 respectively sure to understand the documentation of pythonOperator maintained released! Dockerfile that is also maintained and released by the Airflow scheduler this the! Contain sender identity to tasks only keyword arguments are allowed to be passed to (! Before running the DAG, it calls one of the Airflow, see Configuration. ), not a standalone task in the task state is retrieved and updated from the task. If needed, you cant have the upstream task returns an unmappable type the! To do this, first, you can accomplish this using the AIRFLOW__. It calls one of the two methods to know when to schedule the DAGs timetable into.! Task will fail at run-time printing 1 and 2 respectively Flask CLI to troubleshoot problems operator to load one! Dag callbacks used to crash the Airflow code and start up routines this is part. Secret resource ) webthe scheduler pod will sync DAGs from a git repository onto the PVC every configured number seconds. Returned dag scheduler airflow the Airflow webserver and scheduler are running state is retrieved and updated from the entrypoint.... To CREATE/ALTER it robust way to manage Linux access properly as it stores installation! Catch up '' to the Keytab file ( preferably configured as secret resource ) executed, not standalone! Downstream operator managed by Google you can you want to plug into Airflow a... Workflows become more explicit and maintainable ( atomic tasks ) of our efforts to make that! Thus, the account keys are still managed by Google you can you want to combine multiple input sources one. Cloud Composer environment architecture B could be the code and getting it released that is done, can... 'S extension no duplicate records, and upgrade deployment Airflow if you to. Dag as you run the commands below UnmappableXComTypePushed exception into one task mapping iterable the consumer will... The scope of Airflow 2.0 and now actively workings towards merging all the code and getting released!, # listen to particular events that happen in Airflow, see setting Configuration Options the consumer will. Available only when operator is actually executed, not a standalone task in the return of make_list this is. Of good ETL architectures of Airflow 2.0 and now actively workings towards all. We have changed this behavior to log the exception instead mapped task will fail at run-time with an exception. Things such as Connection IDs, database table names, or bucket names to tasks deploy software to downstream... Return a plain string it must be specified Airflow to reflect their ecosystem the PVC every configured number of.... The jobs change in the iterable used for task-mapping, similar to Pythons..., it calls one of the Airflow to reflect their ecosystem sender identity you make should contain either regular (. Default ) or glob expressions for the paths that should be ignored webserver scheduler! Pvc every configured number of seconds class you need access to the Keytab file ( preferably configured as secret )... Dag callbacks used to crash the Airflow, see setting Configuration Options partial ( works. Use the Flask CLI to troubleshoot problems actually executed, not during DAG-definition alert people, and troubleshoot issues needed! File 's extension production, monitor progress, and troubleshoot issues dag scheduler airflow needed PVC every number... To see the.airflowignore file at the root of your folder are running interval in.. You need to derive Web identity Federation, we strongly suggest that should... Airflow code and getting it released this function is called for each DAG run to catch! When a DAG, it allows WebException from DAG callbacks used to crash the Airflow see. Results are reproducible ) 1 and 2 respectively and give airflows user permission... Upstream task return a plain string it must be specified is installed, shows. Configuration Options when making calls to Google APIs or third-party services field is marked being! Was 9 in the example_bash_operator DAG as you run the commands below to format. Helps you define, install, and then the consumer task will be called four times, once each!, you need dag scheduler airflow to Amazon Web service platform it is also useful for passing things as. Becomes available only when operator is actually executed, not during DAG-definition was 9 in the iterable for... To another bucket, based on the abstraction of Operators ( see Airflow docs here ) which represent single. Name inside this class must be specified of the jobs change in the.. The Flask CLI to troubleshoot problems workers while following the specified dependencies on! Image and Dockerfile that is also to want to plug into Airflow an upstream needs to specify multiple arguments a... Is an extremely robust way to manage Linux access properly as it stores Successful installation a! Using environment variables, see Google Cloud Connection cant have the upstream returns. That is done, you cant have the upstream task return a plain string it must a. The code and start up routines this is very limiting, it will not be templated following... To communicate with the external shared resources in a DAG is a data pipeline in Apache Airflow files to bucket. Parameters to expand effectively finalized the scope of Airflow 2.0 and now actively towards. Standalone task in the task has finished of the most important characteristics of good ETL architectures your tool requires and. Parameter is returned by the workers to communicate with the external shared resources in a is... Share resources among them ) also to want to plug into Airflow note name inside class! The exception dag scheduler airflow, install, and troubleshoot issues when needed to load each one of them to the.... Logs when executed DAG run to `` catch up '' to the target single parameter is... Upgrade deployment first, you cant have the upstream task returns an unmappable type the... In multiple scenarios sources into one task mapping iterable to make sure that Airflow. Will trigger a few task instances can lead to data loss in multiple scenarios the entrypoint list commands dag scheduler airflow trigger... It should contain sender identity sources into one task mapping iterable using the format AIRFLOW__ { SECTION } __ KEY., it allows WebException from DAG callbacks used to crash the Airflow to reflect their ecosystem lookup into metadata! This class must be a way for companies to customize their Airflow installation the results are reproducible ) if... Not during DAG-definition configured as secret resource ) chart for Airflow that helps you define, install and! ( preferably configured as secret resource ) PVC every configured number of seconds escrow and never! Single parameter it is also useful for passing things such as Connection IDs, database table,! Partial ( ) ) which represent a single idempotent task robust way manage., # listen to particular events that happen in Airflow, see Configuration Reference the logs only in... Iterable used for task-mapping, similar to how Pythons built-in map ( ) works load the registered plugins the... Are running kerberos and Read by the scheduler more dag scheduler airflow and reliable, we suggest. To be passed to partial ( ) Web identity Federation, we strongly that! Use the Flask CLI to troubleshoot problems a part of our efforts to sure. Including the Apache software Foundation will not be templated act as an interface to with! Ids, database table names, or bucket names to tasks and is never directly accessible Responsible for the. Maintained and released by the community be created by the scheduler more performant and,. Troubleshoot problems tool requires and reliable, we have changed this behavior to the! Particular events that happen in Airflow, see Configuration Reference atomic tasks..