sparks | A typeface for creating sparklines in text without code

RStudio has support for multiple cursors, which allows you to write and edit multiple lines at the same time.

Example 1

You can simply click Alt on Windows/Linux (or option on Mac) and drag your mouse to make your selection, or you can use Alt+Shift to create a rectangular selection from the current location of the cursor to a clicked position.

Example 2

Another multiple cursor option is for selecting all matching instances of a term. So, you can select names and press Ctrl+Alt+Shift+M. Then, you can use the arrow keys to move the cursors to delete the space and add in the parentheses.

One can achieve this by:

1. Create a dataproc cluster with an environment (your_sample_env) that contains pyspark 3.2 as a package
2. Modify /usr/lib/spark/conf/spark-env.sh by adding

Your first error almost gives you the solution, you have to import spark.implicits._ for Product types.

You could do this:

You can use conditional sum aggregation over a window ordered by sequence like this:

This is a sort of Gaps and Islands problem. You can identify the "island" using conditional cumulative sum by creating a group column, then you can group by CaseNumber + group and aggregate max StartTime and min EndTime for each group:

The error is expected when running Spark in YARN cluster mode but the job doesn't create Spark context. See the source code of ApplicationMaster.scala.

To avoid this error, you need to create a SparkContext or SparkSession, e.g.:

When running jobs in cluster mode, the driver logs are in the Cloud Logging yarn-userlogs. See the doc:

By default, Dataproc runs Spark jobs in client mode, and streams the driver output for viewing as explained, below. However, if the user creates the Dataproc cluster by setting cluster properties to --properties spark:spark.submit.deployMode=cluster or submits the job in cluster mode by setting job properties to --properties spark.submit.deployMode=cluster, driver output is listed in YARN userlogs, which can be accessed in Logging.

Once you can copy io.delta:delta-core_2.12:1.0.0 JAR file to $SPARK_HOME/lib and restart, this error goes away.

This is historic artifact of the fact that initially Spark Avro support was added by Databricks in their proprietary Spark Runtime as com.databricks.spark.avro format, when Sark Avro support was added to open-source Spark as avro format then, for backward compatibility, support of the com.databricks.spark.avro format was retained if spark.sql.legacy.replaceDatabricksSparkAvro.enabled property is set to true:

If it is set to true, the data source provider com.databricks.spark.avro is mapped to the built-in but external Avro data source module for backward compatibility.