MarkRoddy

/* 
  Usage: you'll want to search for the strings <bucket> and <prefix>, and insert the S3 bucket where your access
  logs are being delivered. Use (or delete) <prefix> to filter to a subset of your logs.
*/

 
/*
  These commented out configuration settings you can either run  yourself in the REPL and source this file using
  `.read parse_s3_access_logs.sql`, or you can uncomment them and supply values for yourself.

seddonm1

// need to add the Apache WS XMLSchema library to spark/jars (does not have dependencies)
// https://repo1.maven.org/maven2/org/apache/ws/xmlschema/xmlschema-core/2.2.5/xmlschema-core-2.2.5.jar

import org.apache.ws.commons.schema.XmlSchemaCollection
import java.io.StringReader
import scala.collection.JavaConverters._
import org.apache.ws.commons.schema._
import org.apache.ws.commons.schema.constants.Constants
import org.apache.spark.sql.types._

aialenti

import pandas as pd
from tqdm import tqdm
import csv
import random
import string
from pyspark.sql import SparkSession
from pyspark.sql.functions import *

random.seed(1999)

olofwalker

VS Code Plugins and configuration tips

https://code.visualstudio.com/docs/setup/setup-overview

Plugins

• Project manager https://marketplace.visualstudio.com/items?itemName=alefragnani.project-manager
• Scala metals https://marketplace.visualstudio.com/items?itemName=scalameta.metals

jdegoes

package fpmax

import scala.util.Try
import scala.io.StdIn.readLine

object App0 {
  def main: Unit = {
    println("What is your name?")

    val name = readLine()

ParthaSSatpathy

crocker

package com.databricks.spark.jira

import scala.io.Source

import org.apache.spark.rdd.RDD

import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.sources.{TableScan, BaseRelation, RelationProvider}

dusenberrymw

Spark Tips & Tricks

Misc. Tips & Tricks

• If values are integers in [0, 255], Parquet will automatically compress to use 1 byte unsigned integers, thus decreasing the size of saved DataFrame by a factor of 8.
• Partition DataFrames to have evenly-distributed, ~128MB partition sizes (empirical finding). Always err on the higher side w.r.t. number of partitions.
• Pay particular attention to the number of partitions when using flatMap, especially if the following operation will result in high memory usage. The flatMap op usually results in a DataFrame with a [much] larger number of rows, yet the number of partitions will remain the same. Thus, if a subsequent op causes a large expansion of memory usage (i.e. converting a DataFrame of indices to a DataFrame of large Vectors), the memory usage per partition may become too high. In this case, it is beneficial to repartition the output of flatMap to a number of partitions that will safely allow for appropriate partition memory sizes, based upon the

wsargent

Windows 10 Development Environment for Scala

This is a guide for Scala and Java development on Windows, using Windows Subsystem for Linux, although a bunch of it is applicable to a VirtualBox / Vagrant / Docker subsystem environment. This is not complete, but is intended to be as step by step as possible.

Harden Windows 10

Read the entire Decent Security guide, and follow the instructions, especially:

• Set UAC to full
• Enable Drive Encryption

kayousterhout

Generating Flame Graphs for Apache Spark

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