Apache Spark™ - Unified Engine for large-scale data analytics (original) (raw)
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Install with 'pip'
$ pip install pyspark
$ pyspark
Use the official Docker image
$ docker run -it --rm spark:python3 /opt/spark/bin/pyspark
df = spark.read.json("logs.json")
df.where("age > 21").select("name.first").show()
# Every record contains a label and feature vector
df = spark.createDataFrame(data, ["label", "features"])
# Split the data into train/test datasets
train_df, test_df = df.randomSplit([.80, .20], seed=42)
# Set hyperparameters for the algorithm
rf = RandomForestRegressor(numTrees=100)
# Fit the model to the training data
model = rf.fit(train_df)
# Generate predictions on the test dataset.
model.transform(test_df).show()
df = spark.read.csv("accounts.csv", header=True)
# Select subset of features and filter for balance > 0
filtered_df = df.select("AccountBalance", "CountOfDependents").filter("AccountBalance > 0")
# Generate summary statistics
filtered_df.summary().show()
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$ docker run -it --rm spark /opt/spark/bin/spark-sql
spark-sql>
SELECT
name.first AS first_name,
name.last AS last_name,
age
FROM json.`logs.json`
WHERE age > 21;
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$ docker run -it --rm spark /opt/spark/bin/spark-shell
scala>
val df = spark.read.json("logs.json")
df.where("age > 21")
.select("name.first").show()
Run now
$ docker run -it --rm spark /opt/spark/bin/spark-shell
scala>
Dataset df = spark.read().json("logs.json");
df.where("age > 21")
.select("name.first").show();
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$ docker run -it --rm spark:r /opt/spark/bin/sparkR
>
df <- read.json(path = "logs.json")
df <- filter(df, df$age > 21)
head(select(df, df$name.first))