Merge branch 'amarrella-master'
* amarrella-master: Add note on DataFrame recomme^Cation over RDD Add more Spark DataFrame examples Move DataFrames before RDDs Added DataFrames section and cleared outputs Added DataFrames sectionpull/30/head
Donne Martin
commit
4e8f427764
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@ -15,6 +15,7 @@
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"\n",
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"* IPython Notebook Setup\n",
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"* Python Shell\n",
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"* DataFrames\n",
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"* RDDs\n",
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"* Pair RDDs\n",
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"* Running Spark on a Cluster\n",
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@ -91,12 +92,374 @@
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"sc"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## DataFrames"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"From the following [reference](https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html):\n",
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"\n",
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"A DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Create a DataFrame from JSON files on S3:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"users = context.load(\"s3n://path/to/users.json\", \"json\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Create a new DataFrame that contains “young users” only:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"young = users.filter(users.age<21)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Alternatively, using Pandas-like syntax:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"young = users[users.age<21]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Increment everybody’s age by 1:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"young.select(young.name, young.age+1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Count the number of young users by gender:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"young.groupBy(\"gender\").count()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Join young users with another DataFrame called logs:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"young.join(logs, logs.userId == users.userId, \"left_outer\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Count the number of users in the young DataFrame:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"young.registerTempTable(\"young\")\n",
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"context.sql(\"SELECT count(*) FROM young\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Convert Spark DataFrame to Pandas:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"pandas_df = young.toPandas()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Create a Spark DataFrame from Pandas:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"spark_df = context.createDataFrame(pandas_df)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Given the Spark Context, create a SQLContext:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from pyspark.sql import SQLContext\n",
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"sqlContext = SQLContext(sc)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Create a DataFrame based on the content of a file:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"df = sqlContext.jsonFile(\"file:/path/file.json\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Display the content of the DataFrame:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"df.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Print the schema:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"df.printSchema()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Select a column:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"df.select(\"column_name\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Create a DataFrame with rows matching a given filter:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"df.filter(df.column_name>10)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Aggregate the results and count:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"df.groupBy(\"column_name\").count()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Convert a RDD to a DataFrame (by inferring the schema):"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"df = sqlContext.inferSchema(my_data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Register the DataFrame as a table:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"df.registerTempTable(\"dataframe_name\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Run a SQL Query on a DataFrame registered as a table:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"rdd_from_df = sqlContext.sql(\"SELECT * FROM dataframe_name\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## RDDs\n",
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"\n",
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"Note: RDDs are included for completeness. In Spark 1.3, DataFrames were introduced which are recommended over RDDs. Check out the [DataFrames announcement](https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html) for more info.\n",
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"\n",
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"Resilient Distributed Datasets (RDDs) are the fundamental unit of data in Spark. RDDs can be created from a file, from data in memory, or from another RDD. RDDs are immutable.\n",
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"\n",
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"There are two types of RDD operations:\n",
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@ -1302,21 +1665,21 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"display_name": "Python 3",
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"language": "python",
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"name": "python2"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.10"
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"pygments_lexer": "ipython3",
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"version": "3.4.3"
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}
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},
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"nbformat": 4,
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