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How to check Spark version?

Spark/Big Datahard0.4 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-25

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Check Spark version: In PySpark: `spark.version` or `spark.sparkContext.version`. Scala: `org.apache.spark.SparkContext.SPARK_VERSION`....

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Asked at 1 company
Category
452
questions in Spark/Big Data
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Expert Answer
84 words

Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Check Spark version: In PySpark: spark.version or spark.sparkContext.version. Scala: org.apache.spark.SparkContext.SPARK_VERSION. Shell: spark-submit --version or pyspark --version. Databricks: Runtime version in cluster config (e.g., 13.3 LTS = Spark 3.4). Best practice: Pin version in production; test upgrades in staging; check release notes for breaking changes.

Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.

⚡
Pro Tip

Red Flag: Unpinned runtime. Pro-Move: 'Pin version; test upgrade; release notes.'

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