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The Accessibility Manager for AutoCAD Free Download has a number of features and is a possible aid to screen readers. References Further reading External links Category:Computer-aided design software Category:Digital SLR cameras Category:GIS software Category:Geographic information systems software Category:Screenshot applications Category:Windows-only software Category:Windows graphics-related software Category:Product lifecycle management software Category:Film and video technology Category:3D graphics software Category:3D graphics APIs Category:1990 software Category:Graphical user interfaces Category:AutoGUIQ: Mapreduce in Spark I have a partitioned RDD. rdd = sc.textFile(input) ... It is partitioned on 2 fields. rdd = rdd.partitionBy(field1,field2) In this case is it possible to map/reduce only some of the partitions? A: As far as I know Spark MapReduce works in the best case for parallelizing all the operations that it will do during the execution. If you're going to reduce the result to a single value per partition, I'm afraid there's no way to do this in MapReduce in Spark, as it's not stateless and doesn't support multiple operations per task. In your case, the best solution would be to create a custom data structure in the mapper, that represents the result that you want to store in your RDD. EDIT: As suggested by @Suman as well, the best option in case of Texts is to write your own DataFrame. Here's an example of how to do that: val df = sc.parallelize(Map(10 -> "a", 20 -> "b")).toDF("key", "value") df.createOrReplaceTempView("table") Then you can fetch the data from the RDD with Spark SQL: val dataRDD = sc.sql("SELECT * FROM table") A: It is not possible to map only some partitions. The best you can do is, Load data in your RDD into memory Apply reduce function on your RDD Save result to some storage To do this you need to convert your partitioned RDD into memory e315de8065
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