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Install PySpark
The current Apache Spark installation documentation lists Python 3.10 or newer and Java 17 or newer. Java must be available through a correctly configured JAVA_HOME.
python -m venv .venv
source .venv/bin/activate
pip install pysparkOn Windows PowerShell, activate the environment with ..venvScriptsActivate.ps1. Install only the optional extra that matches your workload:
pip install "pyspark[sql]"
pip install "pyspark[pandas_on_spark]"
pip install "pyspark[connect]"
pip install "pyspark[ml]"Use the standard package for core DataFrame work; the extras add dependencies for SQL support, pandas API on Spark, Spark Connect or MLlib.
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Create a Spark application
from pyspark.sql import SparkSession
spark = (
SparkSession.builder
.appName("example")
.getOrCreate()
)getOrCreate() reuses an existing session when one is available, which is convenient in notebooks and tests. Stop a standalone application when its work is complete:
spark.stop()Create and inspect DataFrames
Create from Python rows
from pyspark.sql import Row
rows = [
Row(id=1, category="a", value=10),
Row(id=2, category="b", value=20),
]
df = spark.createDataFrame(rows)createDataFrame also accepts common row-like Python structures, pandas DataFrames and RDDs. Supply a schema explicitly when stable names and data types matter, especially for empty data or production pipelines.
from pyspark.sql.types import StructType, StructField, LongType, StringType
schema = StructType([
StructField("id", LongType(), nullable=False),
StructField("category", StringType(), nullable=True),
StructField("value", LongType(), nullable=True),
])
df = spark.createDataFrame([(1, "a", 10), (2, "b", 20)], schema)Inspect rows, columns and types
df.printSchema()
df.show()
df.show(5, truncate=False)
df.columns
df.dtypes
df.select("id", "value").show()
show, like count and collect, is an action: it causes Spark to evaluate the required plan.
Transformations and actions
Transformations describe a new DataFrame and are evaluated lazily. Actions request a result, write output or otherwise trigger execution. A chain of select, filter, withColumn, join and groupBy calls therefore builds a plan without immediately processing every row.
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| Transformations (lazy) | Actions (execute the plan) |
|---|---|
select, filter, where, withColumn, drop, join, groupBy, orderBy |
show, count, collect, first, take, write |
Keep transformations readable and trigger an action only when you need to inspect, return or persist the result. Calling collect() brings all returned rows to the driver, so a large result can exhaust driver memory.
Common DataFrame transformations
from pyspark.sql import functions as F
clean = (
df
.filter(F.col("value") > 0)
.withColumn("value_doubled", F.col("value") * 2)
.select("id", "category", "value_doubled")
)
clean.show()Select and rename
df.select("id", F.col("value").alias("amount"))
df.withColumnRenamed("category", "group")Filter rows
df.filter(F.col("value") >= 10)
df.where((F.col("category") == "a") | F.col("category").isNull())Use column expressions rather than Python and, or and not; combine Spark conditions with &, | and ~.
Handle nulls and duplicates
df.fillna({"category": "unknown", "value": 0})
df.dropna(subset=["id"])
df.dropDuplicates(["id"])Sort and limit
df.orderBy(F.col("value").desc()).limit(10)Group, aggregate and pivot
summary = (
clean
.groupBy("category")
.agg(
F.count("*").alias("rows"),
F.avg("value_doubled").alias("avg_value"),
F.sum("value_doubled").alias("total_value"),
)
)
summary.show()Other frequently used aggregates include min, max, countDistinct, first and collect_list. A pivot turns category values into columns:
df.groupBy("category").pivot("status").agg(F.sum("value"))Joins
joined = left.join(right, on="id", how="left")The key named in on must exist on both sides. Choose the join type according to which unmatched rows you need:
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| Join type | Result |
|---|---|
inner |
Only keys present in both DataFrames |
left |
Every row from the left, with matching right-side values when available |
right |
Every row from the right, with matching left-side values when available |
full |
All keys from both sides |
left_semi |
Left rows that have a match; no right columns |
left_anti |
Left rows with no match |
For different key names or compound conditions, use an expression:
joined = left.alias("l").join(
right.alias("r"),
(F.col("l.customer_id") == F.col("r.id")) & (F.col("l.region") == F.col("r.region")),
"inner",
)Window functions
Windows calculate values across related rows without collapsing them into one row per group.
from pyspark.sql.window import Window
w = Window.partitionBy("category").orderBy(F.col("value").desc())
ranked = df.withColumn("rank", F.row_number().over(w))
ranked.show()w = Window.partitionBy("category").orderBy("event_time")
with_running_total = df.withColumn(
"running_total",
F.sum("value").over(w.rowsBetween(Window.unboundedPreceding, Window.currentRow)),
)Common window functions include row_number, rank, dense_rank, lag, lead, sum and avg. Define both the partition and ordering deliberately; ordering ties can produce nondeterministic row-number assignments.
Use Spark SQL with DataFrames
DataFrame expressions and Spark SQL use the same execution engine and can be mixed. Register a temporary view, run SQL, and continue with the DataFrame API:
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df.createOrReplaceTempView("items")
result = spark.sql("""
SELECT category,
COUNT(*) AS rows,
AVG(value) AS avg_value
FROM items
GROUP BY category
""")
result.show()Temporary views last for the lifetime of the session. SQL is often convenient for analyst-authored queries, while the DataFrame API makes Python control flow and reusable expressions easier to compose.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Built-in functions, Python UDFs and pandas UDFs
Prefer built-in functions from pyspark.sql.functions whenever they express the operation. Spark can analyze and optimize these expressions directly.
df.withColumn("normalized", F.lower(F.trim(F.col("category"))))Use a Python UDF only when the required logic cannot be expressed with built-ins. UDFs add Python serialization and dependency considerations, so keep them narrow and test their behavior with nulls and unexpected types.
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
def label(value):
return "high" if value and value > 100 else "normal"
label_udf = udf(label, StringType())
df.withColumn("label", label_udf("value"))Pandas UDFs and mapInPandas process batches through pandas and are useful for vectorized custom logic when supported by your environment. They still require compatible Python dependencies on the workers.
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DataFrame versus RDD
| Choice | Best fit | Trade-off |
|---|---|---|
| DataFrame | Structured rows, joins, aggregations and SQL-style processing | Requires expressing work through columns and schemas |
| RDD | Lower-level distributed collections or operations that need explicit object-level control | Less schema information and fewer optimizer opportunities than DataFrames |
| Spark SQL | SQL text over registered tables and views | Requires SQL syntax and view/table management |
DataFrames are implemented on top of RDDs, but the official quickstart presents DataFrames as the main starting point for structured data. Choose RDDs when the lower-level abstraction is genuinely required.
Read and write data
events = spark.read.parquet("data/events")
events.write.mode("overwrite").parquet("output/events")
csv = spark.read.option("header", True).option("inferSchema", True).csv("data/file.csv")
csv.write.mode("append").option("header", True).csv("output/csv")For production pipelines, define schemas instead of relying on inference when input types must remain stable. Spark writes a directory containing part files and metadata rather than one ordinary local file.
Run locally, with Spark Connect or on a cluster
Local development
The PyPI package is suitable for local development and testing. Start a session with the default local configuration, inspect a small sample, and keep collected results bounded.
Spark Connect
Spark Connect separates the client from the Spark driver. Install the Connect extra when needed and configure the client for the server endpoint documented for your Spark deployment. This adds endpoint, authentication and dependency choices beyond a local session.
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On a cluster, the driver coordinates executors that process partitions. Make Python code and required packages available to workers, use paths visible to the cluster, and size driver memory carefully when collecting or broadcasting data. Exact submission commands and configuration depend on the cluster manager and deployment environment.
Advanced PySpark APIs
- Structured Streaming: DataFrame-style processing for continuously arriving data.
- Pandas API on Spark: pandas-like syntax backed by distributed Spark execution.
- Spark Connect: Client-server access to a remote Spark session.
- MLlib: Distributed machine-learning algorithms and utilities.
These APIs share Spark concepts but have separate dependency, state, deployment or model-management concerns; consult the API documentation for their feature-specific configuration.
Quick Recap
Fast troubleshooting checklist
- Java gateway or startup error: verify Java 17 or later and that
JAVA_HOMEpoints to that installation. - Python version error: use Python 3.10 or newer in the active virtual environment.
- Unexpected type or null behavior: print the schema and provide an explicit schema when creating the DataFrame.
- Slow custom logic: replace a Python UDF with a built-in expression where possible.
- Driver out-of-memory failure: avoid unbounded
collect(); aggregate or write distributed results instead. - Missing worker dependency: install or distribute the same Python packages and code to executors.
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