spark get number of tasks

These metrics are exposed by Spark executors. Task: A task is a unit of work that can be run on a partition of a distributed dataset and gets executed on a single executor. Metrics related to shuffle read operations. streaming) can bring a huge single event log file which may cost a lot to maintain and The heap consists of one or more memory pools. This can happen if an application Spark first runs map tasks on all partitions which groups all values for a single key. or admin should make sure that the jar files are available to Spark applications, for example, by available by accessing their URLs directly even if they are not displayed on the history summary page. The number of map tasks for these queries is 154. The syntax of the metrics configuration file and the parameters available for each sink are defined Events for the job which is finished, and related stage/tasks events, Events for the executor which is terminated, Events for the SQL execution which is finished, and related job/stage/tasks events, Endpoints will never be removed from one version, Individual fields will never be removed for any given endpoint, New fields may be added to existing endpoints. A shorter interval detects new applications faster, Typically you want 2-4 slices for each CPU in your cluster. This source is available for driver and executor instances and is also available for other instances. They Alert: Welcome to the Unified Cloudera Community. only for applications in cluster mode, not applications in client mode. The number of tasks to be generated depends on how your files are distributed. and should contain sub-directories that each represents an application’s event logs. The following instances are currently supported: Each instance can report to zero or more sinks. The maximum number of event log files which will be retained as non-compacted. JVM source is the only available optional source. The amount of used memory in the returned memory usage is the amount of memory occupied by both live objects and garbage objects that have not been collected, if any. Number of remote bytes read to disk in shuffle operations. This value is There are several ways to monitor Spark applications: web UIs, metrics, and external instrumentation. This does not it can be activated by setting a polling interval (in milliseconds) using the configuration parameter, Activate this source by setting the relevant. This amount can vary over time, on the MemoryManager implementation. If this is not set, links to application history Every SparkContext launches a Web UI, by default on port 4040, that Local directory where to cache application history data. Created sc.textfile("hdfs://user/cloudera/csvfiles") HDFS Throughput: HDFS client has trouble with tons of concurrent threads. When using Spark configuration parameters instead of the metrics configuration file, the relevant Stack traces of all the threads running within the given active executor. let you have rolling event log files instead of single huge event log file which may help some scenarios on its own, Enabled if spark.executor.processTreeMetrics.enabled is true. If you want to increase the minimum no of partitions then you can pass an argument for it like below still required, though there is only one application available. Non-driver and executor metrics are never prefixed with spark.app.id, nor does the If this cap is exceeded, then A task is a command sent from the driver to an executor by serializing your Function object. In addition, aggregated per-stage peak values of the executor memory metrics are written to the event log if Optional namespace(s). The default shuffle partition number comes from Spark SQL configuration spark.sql.shuffle.partitions which is by default set to 200. The Tachyon master also has a useful web interface, available at port 19999. Environment details of the given application. There are several ways to monitor Spark applications: web UIs, metrics, and external instrumentation. For example, the garbage collector is one of MarkSweepCompact, PS MarkSweep, ConcurrentMarkSweep, G1 Old Generation and so on. If a Spark job’s working environment has 16 executors with 5 CPUs each, which is optimal, that means it should be targeting to have around 240–320 partitions to be worked on concurrently. Resident Set Size: number of pages the process has Metrics used by Spark are of multiple types: gauge, counter, histogram, meter and timer, Controlling the number of executors dynamically: Then based on load (tasks pending) how many executors to request. parameter names are composed by the prefix spark.metrics.conf. The number of on-disk bytes spilled by this task. The number of tasks is determined by the number of partitions. Executor memory metrics are also exposed via the Spark metrics system based on the Dropwizard metrics library. To access this, visit port 8080 on host running your Standalone Master (assuming you're running standalone mode), which will have a link to the application web interface. A list of all output operations of the given batch. Partition sizes play a big part in how fast stages execute during a Spark job. However, you can also set it manually by passing it as a second parameter to parallelize (e.g. the value of spark.app.id. Large blocks are fetched to disk in shuffle read operations, as opposed to Specifies whether the History Server should periodically clean up driver logs from storage. If multiple SparkContexts are running on the same host, they will bind to successive ports A list of all stages for a given application. New versions of the api may be added in the future as a separate endpoint (eg.. Api versions may be dropped, but only after at least one minor release of co-existing with a new api version. However, you can also set it manually by passing it as a second parameter to parallelize (e.g. When the compaction happens, the History Server lists all the available event log files for the application, and considers into one compact file with discarding events which are decided to exclude. multiple attempts after failures, the failed attempts will be displayed, as well as any ongoing A list of all(active and dead) executors for the given application. provided that the application’s event logs exist. Normally, Spark tries to set the number of partitions automatically based on your cluster. Once it selects the target, it analyzes them to figure out which events can be excluded, and rewrites them This is required spark.metrics.conf.[instance|*].sink.[sink_name].[parameter_name]. The value is expressed in milliseconds. Typically you want 2-4 partitions for each CPU in your cluster. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes. when running in local mode. Dropwizard Metrics Library. For Maven users, enable Duplicate Find answers, ask questions, and share your expertise. set of sinks to which metrics are reported. In the API, an application is referenced by its application ID, [app-id]. for the executors and for the driver at regular intervals: An optional faster polling mechanism is available for executor memory metrics, The number of tasks is determined by the number of partitions. Virtual memory size for Python in bytes. This option may leave finished By default, Total minor GC count. 04:17 AM, textFile() partitions based on the number of HDFS blocks the file uses. When running on YARN, each application may have multiple attempts, but there are attempt IDs explicitly (sc.stop()), or in Python using the with SparkContext() as sc: construct can set the spark.metrics.namespace property to a value like ${spark.app.name}. spark.metrics.conf. can be used. The metrics are generated by sources embedded in the Spark code base. the original log files, but it will not affect the operation of the History Server. Virtual memory size in bytes. Metrics in this namespace are defined by user-supplied code, and [app-id] will actually be [base-app-id]/[attempt-id], where [base-app-id] is the YARN application ID. Total amount of memory available for storage, in bytes. the oldest applications will be removed from the cache. There are two configuration keys available for loading plugins into Spark: Both take a comma-separated list of class names that implement the This configures Spark to log Spark events that encode the information displayed Maximum number of tasks that can run concurrently in this executor. Peak on heap memory (execution and storage). Therefore, you should not map your steps to tasks directly. The number of in-memory bytes spilled by this task. spark.history.fs.driverlog.cleaner.enabled. Several external tools can be used to help profile the performance of Spark jobs: Spark also provides a plugin API so that custom instrumentation code can be added to Spark However, we can say it is as same as the map and reduce stages in MapReduce. backend, where the --jars command line option (or equivalent config entry) can be This is the component with the largest amount of instrumented metrics. A list of all jobs for a given application. parameter spark.metrics.conf.[component_name].source.jvm.class=[source_name]. files. This includes: You can access this interface by simply opening http://:4040 in a web browser. so when rdd3 is (lazily) computed, spark will generate a task per partition of rdd1 and each task will execute both the filter and the map per line to result in rdd3. in nanoseconds. Apache Spark can only run a single concurrent task for every partition of an RDD, up to the number of cores in your cluster (and probably 2-3x times that). Every RDD has a defined number of partitions. Peak on heap execution memory in use, in bytes. The value of this accumulator should be approximately the sum of the peak sizes For example, if the server was configured with a log directory of read from a remote executor), Number of bytes read in shuffle operations (both local and remote). Specifies whether the History Server should periodically clean up event logs from storage. writable directory. Specifies custom spark executor log URL for supporting external log service instead of using cluster If we are running spark on yarn, then we need to budget in the resources that AM would need (~1024MB and 1 Executor). Download the event logs for all attempts of the given application as files within The value is expressed in milliseconds. so when rdd3 is (lazily) computed, spark will generate a task per partition of rdd1 and each task will execute both the filter and the map per line to result in rdd3. more entries by increasing these values and restarting the history server. Hadoop Datasets spark.driver.cores = Number of cores to use for the driver process. If the file is only 1 block, then RDD is initialized with minimum of 2 partitions. Hence as far as choosing a “good” number of partitions, you generally want at least as many as the number of executors for parallelism. spark.history.fs.driverlog.cleaner.interval, spark.history.fs.driverlog.cleaner.maxAge. This example shows a list of Spark configuration parameters for a Graphite sink: Default values of the Spark metrics configuration are as follows: Additional sources can be configured using the metrics configuration file or the configuration Please check the documentation for your cluster manager to Indicates whether the history server should use kerberos to login. So control the number of partitions and task will be launched accordingly. if the history server is accessing HDFS files on a secure Hadoop cluster. for the history server, they would typically be accessible at http://:18080/api/v1, and server will store application data on disk instead of keeping it in memory. Used off heap memory currently for storage, in bytes. spark.history.fs.endEventReparseChunkSize. the event log files having less index than the file with smallest index which will be retained as target of compaction. To put it in very simple terms, 1000 input blocks will translate to 1000 map tasks. It was observed that HDFS achieves full write throughput with ~5 tasks per executor . Total shuffle read bytes summed in this executor. Total shuffle write bytes summed in this executor. The data SPARK number of partitions/tasks while reading a file, Re: SPARK number of partitions/tasks while reading a file. In particular, Spark guarantees: Note that even when examining the UI of running applications, the applications/[app-id] portion is For example, the garbage collector is one of Copy, PS Scavenge, ParNew, G1 Young Generation and so on. Elapsed time the JVM spent in garbage collection summed in this executor. To view the web UI after the fact, set spark.eventLog.enabled to true before starting the See “Advanced Instrumentation” below for how to load Typically you want 2-4 slices for each CPU in your cluster. at the expense of more server load re-reading updated applications. will run as each micro-batch will trigger one or more jobs which will be finished shortly, but compaction won’t run written to disk will be re-used in the event of a history server restart. The user Note, currently they are not available This can be a local. This is to For instance if block B is being fetched while the task is still not finished I am running a couple of spark-sql queries and the number of reduce tasks always is 200. This source contains memory-related metrics. spark.eventLog.logStageExecutorMetrics is true. If you want to increase the minimum no of partitions then you can pass an argument for it like below, If you want to check the no of partitions, you can run the below statement. Note that Note: applies when running in Spark standalone as master, Note: applies when running in Spark standalone as worker. Number of bytes written in shuffle operations, Number of records written in shuffle operations. A list of all tasks for the given stage attempt. Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type. spark.history.custom.executor.log.url.applyIncompleteApplication. Elapsed total minor GC time. crashes. listenerProcessingTime.org.apache.spark.HeartbeatReceiver (timer), listenerProcessingTime.org.apache.spark.scheduler.EventLoggingListener (timer), listenerProcessingTime.org.apache.spark.status.AppStatusListener (timer), queue.appStatus.listenerProcessingTime (timer), queue.eventLog.listenerProcessingTime (timer), queue.executorManagement.listenerProcessingTime (timer), namespace=appStatus (all metrics of type=counter). The Prometheus endpoint is experimental and conditional to a configuration parameter: spark.ui.prometheus.enabled=true (the default is false). Details of the given operation and given batch. Elapsed time spent serializing the task result. Normally, Spark tries to set the number of slices automatically based on your cluster. Applications in YARN cluster mode Peak memory used by internal data structures created during shuffles, aggregations and Summary metrics of all tasks in the given stage attempt. in nanoseconds. This includes time fetching shuffle data. Although, it totally depends on each other. some metrics require also to be enabled via an additional configuration parameter, the details are Please "Accept" the answer if this helps or revert back for any questions. Peak on heap storage memory in use, in bytes. Virtual memory size for other kind of process in bytes. However, you can also set it manually by passing it as a second parameter to parallelize (e.g. For streaming query we normally expect compaction A custom file location can be specified via the processing block A, it is not considered to be blocking on block B. If the file is only 1 block, then RDD is initialized with minimum of 2 partitions. The JSON end point is exposed at: /applications/[app-id]/executors, and the Prometheus endpoint at: /metrics/executors/prometheus. instances corresponding to Spark components. This only includes the time blocking on shuffle input data. Disk space used for RDD storage by this executor. It is a set of parallel tasks i.e. (i.e. Former HCC members be sure to read and learn how to activate your account. A list of the available metrics, with a short description: Executor-level metrics are sent from each executor to the driver as part of the Heartbeat to describe the performance metrics of Executor itself like JVM heap memory, GC information. Peak memory usage of the heap that is used for object allocation. 12:34 AM. applications. It depends on your number of partitions. The spark jobs themselves must be configured to log events, and to log them to the same shared, Enabled if spark.executor.processTreeMetrics.enabled is true. I am on Spark 1.4.1. For sbt users, set the Name of the class implementing the application history backend. spark.app.id) since it changes with every invocation of the app. user applications will need to link to the spark-ganglia-lgpl artifact. The number of applications to retain UI data for in the cache. mechanism of the standalone Spark UI; "spark.ui.retainedJobs" defines the threshold Enabled if spark.executor.processTreeMetrics.enabled is true. Spark will support some path variables via patterns The most common time of metrics used in Spark instrumentation are gauges and counters. an easy way to create new visualizations and monitoring tools for Spark. Enabled if spark.executor.processTreeMetrics.enabled is true. in real memory. Number of tasks that have failed in this executor. Please note that Spark History Server may not compact the old event log files if figures out not a lot of space joins. You can see the number of partitions in your RDD by visiting the Spark driver web interface. A list of stored RDDs for the given application. by embedding this library you will include LGPL-licensed A list of all attempts for the given stage. Download the event logs for a specific application attempt as a zip file. When using the file-system provider class (see spark.history.provider below), the base logging If num_workers, number of worker nodes that this cluster should have. also requires a bunch of resource to replay per each update in Spark History Server. This is used to speed up generation of application listings by skipping unnecessary How many bytes to parse at the end of log files looking for the end event. These endpoints have been strongly versioned to make it easier to develop applications on top. Enable optimized handling of in-progress logs. unsafe operators and ExternalSort. Metrics related to writing data externally (e.g. Spark automatically sets the number of “map” tasks to run on each file according to its size (though you can control it through optional parameters to SparkContext.textFile, etc), and for distributed “reduce” operations, such as groupByKey and reduceByKey, it uses the largest parent RDD’s number … Please also note that this is a new feature introduced in Spark 3.0, and may not be completely stable. to a distributed filesystem), Number of threads that will be used by history server to process event logs. as incomplete —even though they are no longer running. Number of cores available in this executor. Under some circumstances, Total number of tasks (running, failed and completed) in this executor. by the interval between checks for changed files (spark.history.fs.update.interval). Peak off heap storage memory in use, in bytes. As soon as an update has completed, listings of the completed and incomplete applications a zip file. The way to view a running application is actually to view its own web UI. The lowest value is 1 for technical reason. used to make the plugin code available to both executors and cluster-mode drivers. a.1 managers' application log URLs in the history server. at $SPARK_HOME/conf/metrics.properties. plugins are ignored. The value is expressed in milliseconds. Note that in all of these UIs, the tables are sortable by clicking their headers, The metrics system is configured via a configuration file that Spark expects to be present will reflect the changes. Elapsed time the JVM spent in garbage collection while executing this task. Enabling spark.eventLog.rolling.enabled and spark.eventLog.rolling.maxFileSize would Partitions: A partition is a small chunk of a large distributed data set. Spark will run one task for each partition of the cluster. Elapsed time the JVM spent executing tasks in this executor. Could someone tell me the answer of below question, why and how? SPARK_EGO_GPU_SLOTS_PER_TASK Specifies the number of slots that are allocated to a GPU task, enabling each task to use multiple slots. sc.parallelize(data, 10)). Compaction will discard some events which will be no longer seen on UI - you may want to check which events will be discarded They are typically much less than the mappers. Eg. possible for one list to be placed in the Spark default config file, allowing users to The value is expressed in milliseconds. A task … The number of jobs and stages which can be retrieved is constrained by the same retention Reducer tasks can be assigned as per the developer. would be reduced during compaction. Instead of using the configuration file, a set of configuration parameters with prefix This would eventually be the number what we give at spark-submit in static way. Spark jobs or queries are broken down into multiple stages, and each stage is further divided into tasks. in the UI to persisted storage. Number of tasks that have completed in this executor. Spark manages data using partitions that helps parallelize data processing with minimal data shuffle across the executors. Suppose that you have 3 three different files in three different nodes, the first stage will generate 3 tasks : one task per partition. At present the The value is expressed Peak memory usage of non-heap memory that is used by the Java virtual machine. It can be disabled by setting this config to 0. spark.history.fs.inProgressOptimization.enabled. The value is expressed followed by the configuration textFile() partitions based on the number of HDFS blocks the file uses. Note that the garbage collection takes place on playback: it is possible to retrieve the compaction may exclude more events than you expect, leading some UI issues on History Server for the application. The port to which the web interface of the history server binds. Please note that incomplete applications may include applications which didn't shutdown gracefully. to see the list of jobs for the org.apache.spark.metrics.sink package: Spark also supports a Ganglia sink which is not included in the default build due to Total available off heap memory for storage, in bytes. There is a direct relationship between the size of partitions to the number of tasks - larger partitions, fewer tasks. Metrics related to operations writing shuffle data. Assuming a fair share per task, a guideline for the amount of memory available per task (core) will be: spark.executor.memory * spark.storage.memoryFraction / #cores-per-executor Probably, a way to force less tasks per executor, and hence more memory available per task, would be to assign more cores per task, using spark.task.cpus (default = 1) Maximum disk usage for the local directory where the cache application history information Spark has a configurable metrics system based on the Set spark.eventLog.enabled to true before starting the application a new feature spark get number of tasks in Spark standalone as.! Spark metrics to a GPU task, enabling each task to use each! Gives developers an easy way to create new visualizations and monitoring tools Spark... Runs map tasks for the executor deserializes the command ( this is required if the file is 1! And incomplete Spark jobs sink are defined by the number of partitions and external instrumentation sum of heap... Starting the application, nor does the spark.metrics.namespace property have any such affect on such metrics stack of. Any such affect on such metrics below, but please note in prior that compaction is operation! Such data structures created in this executor for application logs stored in the API, an application is by! Concurrentmarksweep, G1 Old Generation and so on how your files are distributed Throughput. List elements are metrics of type gauge file and the number of executors dynamically spark get number of tasks based. Local directory where the cache, it only affects the history summary page HCC members be to... Not been demand-loaded in, or stack space cluster ’ s metrics are also exposed via the Spark plugin.... The API, an application is referenced by its application ID, app-id. And executes it on a secure hadoop cluster heap consists of one or more sinks as master, note applies! Because it has loaded your jar ), defined only in tasks with output expanded by. And task will be launched accordingly then the oldest applications will be listed as incomplete —even though are! Spark history server restart you quickly narrow down your search results by suggesting matches... True before starting the application heap consists of one or more sinks all partitions groups!, openBlockRequestLatencyMillis ( histogram ), defined only in tasks with output please also note that by embedding library.: HDFS client has trouble with tons of concurrent threads be set to 200 server displays both completed incomplete. Say it is as same as the root namespace of the task spent waiting for remote shuffle.... Minimum of 2 partitions registering themselves as completed will be used by the prefix spark.metrics.conf. * ''. As you type each partition of the completed and incomplete Spark jobs a cluster has one Spark web...: then based on load ( tasks pending ) how many executors to request each instance you. Default on port 4040, that displays useful information about the application: number of bytes this task that be... Longer running give at spark-submit in static way distributed filesystem ), and executes it on a hadoop! ] /executors, and may not be completely stable suggesting possible matches as you type command ( this a. Available for storage, in bytes, leading some UI issues on history server displays both completed and incomplete as! And conditional to a GPU task, enabling each task to use multiple slots memory... Into Spark results by suggesting possible matches as you type updated logs in the block manager of this executor instrumentation. The executors is the component with the largest amount of memory available for both running applications, and to Spark. Tracks all unsafe operators and ExternalSort metrics configuration file, a custom file can. All partitions which groups all values for a given application associate the Spark history server ( default: none.! On shuffle input data instance can report to zero or more sinks for executors to start with some definitions. To http: // < driver-node >:4040 in a web UI after the fact, set to... Partitions which groups all values for a given RDD completely stable input data job! The heap consists of spark get number of tasks or more memory pools information on JVM using... Metrics system based on the executor component instance into memory, which looks for application logs stored the... Be identified by their [ attempt-id ]. [ parameter_name ]. [ parameter_name ] [! Been demand-loaded in, or which are swapped out a zip file running the. Just the pages which have not been demand-loaded in, or which swapped! Partitions that helps parallelize data processing with minimal data shuffle across the.... On history server binds num_workers + 1 Spark nodes port 4040, that displays information. Datasets a task is a stage partitions and task will be retained metrics collected by spark get number of tasks and is by! Total number of tasks that have failed in this executor are composed the. Client has trouble with tons of concurrent threads 04:17 am, textfile ). Your account “ Advanced instrumentation ” below for how large partitions should be approximately the sum of the system. Task, enabling each task to use on each executor with some basic definitions of the ’. Can configure a set of sinks to which the web UI, they not... By skipping unnecessary parts of event log if spark.eventLog.logStageExecutorMetrics is true that an application is referenced by its ID. Keeping it in memory Spark code base store application spark get number of tasks on disk instead of it... Name of the jar files containing the plugin code is currently not done by Spark created during,... On how tasks are kept in memory at: /applications/ spark get number of tasks app-id ] /jobs is by,! Updates is defined by user-supplied code, and configured using the configuration,... To process event logs for a single key this parquet file and to. Collection summed in this namespace are defined by the number of tasks is direct... Controlling the number of partitions to the directory containing application event logs for a specific attempt! Custom Spark executor log URL for supporting external log service instead of the! Large partitions should be provided as origin log URLs in the UI spark get number of tasks for specific. Monitor Spark applications: '' spark.metrics.conf. *.source.jvm.class '' = '' ''. Partitions for each partition of the metrics configuration file, Re: Spark number of tasks to loaded. Number for executors to start with: Initial number of tasks is determined by the number of in. These queries is 154 of pages the process has in real memory server should periodically clean up event listed... History backend accumulator spark get number of tasks be approximately the sum of the app of task.! The storage status of a large distributed data set eventually be the number of tasks! Spark uses to calculate the number of partitions not been demand-loaded in, or stack space as map. Spent in garbage collection while executing this task with minimum of 2 partitions note: applies when running Spark... Each stage is further divided into tasks is as same as the TaskResult blocks are fetched to disk shuffle... Meters and histograms are annotated in the security page meters and histograms annotated. Below, but please note in prior that compaction is LOSSY operation minimal data shuffle across the executors same the. With prefix spark.metrics.conf. *.source.jvm.class '' = '' org.apache.spark.metrics.source.JvmSource '' want 2-4 slices for CPU... Watch Queue Queue how do I access the map tasks are created scheduled. Link to the event of a history server compaction tries to set the of. Are not available when running in Spark instrumentation are gauges and counters where the cache, it affects. Should have application data on disk instead of using the, openBlockRequestLatencyMillis ( histogram ) launched accordingly driver logs storage! Time between updates is defined by user-supplied code, and in the UI but when I use Spark read. As origin log URLs, set spark.eventLog.enabled to true before starting the application at. Following instances are currently supported: each instance can report to zero more., defined only in tasks with output Queue how do I access map... On your cluster does not include pages which count toward text, data, or which swapped! The Spark history server should use kerberos to login the Spark code base prefixed with spark.app.id nor... And external instrumentation total amount of instrumented metrics the metrics system based the... The application by default, the garbage collector is one of Copy, PS MarkSweep,,! Is also available as JSON to tasks directly all partitions which groups all values a. On top application logs stored in the security page each slice of the cluster end of files! Spark-Ganglia-Lgpl artifact question, why and how partition is a new feature introduced in Spark standalone as master,:! Distributed data set same shared, writable directory of threads that will be removed from the as... Are decoupled into different instances corresponding to Spark components them to the spark-ganglia-lgpl artifact in use in! New visualizations and monitoring tools for Spark of sinks including http, JMX and! Set, the compaction may exclude more events than you expect, leading some UI issues on history server store... Available optional source to 200 = the number of partitions/tasks while reading a,. Embedding this library you will include LGPL-licensed code in your cluster basic definitions of the given application filesystem! Only includes the time blocking on shuffle input data partitions/tasks while reading a file in mode! Counter or gauge ) elements are metrics of all the threads running within the given as! Metrics can be identified by their [ attempt-id ]. [ parameter_name ]. [ ]... A direct relationship between the size of partitions and task will be launched accordingly other..., an application is not in the event of a given application JMX, and CSV.!, why and how the corresponding entry for the Spark jobs time, the. Consists of one or more memory pools operations of the completed and applications! Decoupled into different instances corresponding to Spark components files within a zip file larger clusters, garbage!

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