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LDA Example: Modeling topics in the Spark documentation
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This example uses Scala. Please see the MLlib documentation for a Java example.
Try running this code in the Spark shell. It may produce different topics each time (since LDA includes some randomization), but it should give topics similar to those listed above.
This example is paired with a blog post on LDA in Spark: http://databricks.com/blog
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Make queries to Elasticsearch from a lambda in python
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Auto Incrementing Sequence in mongodb using php mongo
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Flame graphs are a nifty debugging tool to determine where CPU time is being spent. Using the Java Flight recorder, you can do this for Java processes without adding significant runtime overhead.
When are flame graphs useful?
Shivaram Venkataraman and I have found these flame recordings to be useful for diagnosing coarse-grained performance problems. We started using them at the suggestion of Josh Rosen, who quickly made one for the Spark scheduler when we were talking to him about why the scheduler caps out at a throughput of a few thousand tasks per second. Josh generated a graph similar to the one below, which illustrates that a significant amount of time is spent in serialization (if you click in the top right hand corner and search for "serialize", you can see that 78.6% of the sampled CPU time was spent in serialization). We used this insight to spee
Install & configure ffmpeg, ffprobe on SageMaker (Amazon Linux)
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