So I was able to find all the transcripts for my user. Is it as easy as copying them into their new workstation folder and magically show up? Or is there some import function so they are available on their new spark install?
I'm working with a large text variable, working it into single line JSON where Spark can process beautifully. Using a single node 256 GB 32 core Standard_E32d_v4 "cluster", which should be plenty memory for this dataset (haven't seen cluster memory used exceed 130 GB). However I keep getting crashes "The spark driver has stopped unexpectedly and is restarting..." There is no further info on the failure. This happens when writing an intermediate step to text file using:
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Any insight on what could be causing this? Not sure how else to work around this limitation since I've already broken the pipeline down into an intermediate-step-write, garbage collect/reset memory state, continue from intermediate, flow
I did try using text df. Problem is the string will be in a single row, writes to single row, read back into df in single row (even with newlines I've inserted to create single line JSON). Reason I've been using put then spark.read.json is to convert the text to single line JSON df. I'd be happy going straight from large str >> single line JSON df without the write-text, read-JSON operation but don't know how to do that directly
If you're migrating Spark workloads to Amazon EMR from another platform, we recommend that you test your workloads with the Spark defaults set by Amazon EMR before you add custom configurations. Most customers see improved performance with our default settings.
Glue doesn't support predicate push down for startsWith, contains, or endsWith. If you are using Glue metastore and you encounter errors due to the predicate pushdown for these functions, set this configuration to false.
Setting custom garbage collection configurations with spark.driver.extraJavaOptions and spark.executor.extraJavaOptions results in driver or executor launch failure with Amazon EMR 6.1 because of a conflicting garbage collection configuration with Amazon EMR 6.1.0. For Amazon EMR 6.1.0, the default garbage collection configuration is set through spark.driver.defaultJavaOptions and spark.executor.defaultJavaOptions. This configuration applies only to Amazon EMR 6.1.0. JVM options not related to garbage collection, such as those for configuring logging (-verbose:class), can still be set through extraJavaOptions. For more information, see Spark application properties.
To configure your executors to use the maximum resources possible on each node in a cluster, set maximizeResourceAllocation to true in your spark configuration classification. The maximizeResourceAllocation is specific to Amazon EMR . When you enable maximizeResourceAllocation, Amazon EMR calculates the maximum compute and memory resources available for an executor on an instance in the core instance group. It then sets the corresponding spark-defaults settings based on the calculated maximum values.
Amazon EMR calculates the maximum compute and memory resources available for an executor based on an instance type from the core instance fleet. Since each instance fleet can have different instance types and sizes within a fleet, the executor configuration that Amazon EMR uses might not be the best for your clusters, so we don't recommend using the default settings when using maximum resource allocation. Configure custom settings for your instance fleet clusters.
You should not use the maximizeResourceAllocation option on clusters with other distributed applications like HBase. Amazon EMR uses custom YARN configurations for distributed applications, which can conflict with maximizeResourceAllocation and cause Spark applications to fail.
Setting is configured based on the instance types in the cluster. However, because the Spark driver application may run on either the primary or one of the core instances (for example, in YARN client and cluster modes, respectively), this is set based on the smaller of the instance types in these two instance groups.
With Amazon EMR release 5.9.0 and higher, Spark on Amazon EMR includes a set of features to help ensure that Spark gracefully handles node termination because of a manual resize or an automatic scaling policy request. Amazon EMR implements a deny listing mechanism in Spark that is built on top of the YARN decommissioning mechanism. This mechanism helps ensure that no new tasks are scheduled on a node that is decommissioning, while at the same time allowing tasks that are already running to complete. In addition, there are features to help recover Spark jobs faster if shuffle blocks are lost when a node terminates. The recomputation process is triggered sooner and optimized to recompute faster with fewer stage retries, and jobs can be prevented from failing because of fetch failures that are caused by missing shuffle blocks.
The spark.decommissioning.timeout.threshold setting was added in Amazon EMR release 5.11.0 to improve Spark resiliency when you use Spot instances. In earlier releases, when a node uses a Spot instance, and the instance is terminated because of bid price, Spark may not be able to handle the termination gracefully. Jobs may fail, and shuffle recomputations could take a significant amount of time. For this reason, we recommend using release 5.11.0 or later if you use Spot instances.
When set to true, Spark deny lists nodes that are in the decommissioning state in YARN. Spark does not schedule new tasks on executors running on that node. Tasks already running are allowed to complete.
The amount of time that a node in the decommissioning state is deny listed. By default, this value is set to one hour, which is also the default for yarn.resourcemanager.decommissioning.timeout. To ensure that a node is deny listed for its entire decommissioning period, set this value equal to or greater than yarn.resourcemanager.decommissioning.timeout. After the decommissioning timeout expires, the node transitions to a decommissioned state, and Amazon EMR can terminate the node's EC2 instance. If any tasks are still running after the timeout expires, they are lost or killed and rescheduled on executors running on other nodes.
Available in Amazon EMR release 5.11.0 or later. Specified in seconds. When a node transitions to the decommissioning state, if the host will decommission within a time period equal to or less than this value, Amazon EMR not only deny lists the node, but also cleans up the host state (as specified by spark.resourceManager.cleanupExpiredHost) without waiting for the node to transition to a decommissioned state. This allows Spark to handle Spot instance terminations better because Spot instances decommission within a 20-second timeout regardless of the value of yarn.resourcemager.decommissioning.timeout, which may not provide other nodes enough time to read shuffle files.
When set to true, helps prevent Spark from failing stages and eventually failing the job because of too many failed fetches from decommissioned nodes. Failed fetches of shuffle blocks from a node in the decommissioned state will not count toward the maximum number of consecutive fetch failures.
Navigate to the new Amazon EMR console and select Switch to the old console from the side navigation. For more information on what to expect when you switch to the old console, see Using the old console.
With Amazon EMR version 5.21.0 and later, you can override cluster configurations and specify additional configuration classifications for each instance group in a running cluster. You do this by using the Amazon EMR console, the AWS Command Line Interface (AWS CLI), or the AWS SDK. For more information, see Supplying a Configuration for an Instance Group in a Running Cluster.
Apache Spark releases 3.2.x and earlier use the legacy Apache Log4j 1.x and the log4j.properties file to configure Log4j in Spark processes. Apache Spark releases 3.3.0 and later use Apache Log4j 2.x and the log4j2.properties file to configure Log4j in Spark processes.
If you have configured Apache Spark Log4j using an Amazon EMR release lower than 6.8.0, then you must remove the legacy spark-log4j configuration classification and migrate to the spark-log4j2 configuration classification and key format before you can upgrade to Amazon EMR 6.8.0 or later. The legacy spark-log4j classification causes cluster creation to fail with a ValidationException error in Amazon EMR releases 6.8.0 and later. You will not be charged for a failure related to the Log4j incompatibility, but you must remove the defunct spark-log4j configuration classification to continue.
With Amazon EMR , Apache Spark uses a log4j2.properties file rather than the .xml file described in the Apache Log4j Migration Guide. Also, we do not recommend using the Log4j 1.x bridge method to convert to Log4j 2.x.
Necessary to start any gas-fueled combustion engine, spark plugs send high voltage electricity to one end and ignite a spark at the other end. The spark fires the air and fuel mixture within the engine and creates the combustion that powers your car. Read on to learn more about the components that make up a spark plug.
Connects to the terminal by an internal wire. Tip is made of copper, nickel, chromium or other precious metals. The metal carries the high voltage through the spark plug so it can spark when it goes across the small gap between the central electrode and the side electrode.
The spark plugs are typically located at the top of the cylinder head. The piston moves down the cylinder where it take in a combination of air and fuel. Next, the piston travels back up to the spark plug, compressing the mixture.
When the piston is at top dead center, the ignition coil sends voltage out to the spark plug generating a spark and firing off the air/fuel mixture. The piston then travels back down, generating power for the vehicle. The piston then travels back to the top and pushes the exhaust out on its way up. At this point, the whole process starts over again. Thanks to this combustion event, your vehicle has the power it needs to run. 152ee80cbc
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