Softwr

Technology · head to head

Apache Spark vs Sourcegraph

Apache Spark logo

Apache Spark

Technology

A distributed engine for batch, SQL, streaming and machine learning workloads over data that does not fit on one machine.

From
Free
Free plan
Yes
Full reviewWebsite
Sourcegraph logo

Sourcegraph

Developer Tools

Code search and intelligence platform for large codebases with Cody AI assistant

From
$16000/year
Free plan
No
Full reviewWebsite

The short version

  • Only Apache Spark has a free tier, so it costs nothing to try first.
  • Each has a real cost: Apache Spark running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.; Sourcegraph enterprise pricing starting at $16k annually creates significant barrier for small teams
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Spark and Sourcegraph actually diverge.

Attributes where Apache Spark and Sourcegraph differ
AttributeApache SparkSourcegraph
Starting priceFree$16000/year
Pricing modelopen-sourcesubscription
Free tierYesNo
PlatformsWebCloud, Self-hosted
CategoryTechnologyDeveloper Tools

Identical on both: user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Spark

  • Unified engine
  • Catalyst optimiser
  • DataFrame and SQL APIs
  • Structured Streaming
  • Spark Connect
  • Kubernetes and YARN support
  • Table format integration
  • MLlib

Only in Sourcegraph

Nothing recorded that Apache Spark does not also cover.

What people use each for

The jobs each tool is most often brought in to do.

Apache Spark

  • Nightly ETL over terabytes in object storage, where a single machine would take longer than the batch window allowsnot Sourcegraph
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Sourcegraph
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot Sourcegraph
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Sourcegraph

Sourcegraph

  • Large-scale code migrations across hundreds of repositoriesnot Apache Spark
  • Enterprise governance and code ownership trackingnot Apache Spark
  • AI-assisted bug hunting and security pattern detectionnot Apache Spark
  • Onboarding engineers into large unfamiliar codebasesnot Apache Spark
  • Regulatory compliance and code audit trailsnot Apache Spark

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Spark

  • Running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query.
  • The fastest Spark is not open source. Databricks' Photon engine and comparable vendor accelerations are proprietary, so benchmark numbers quoted for Spark frequently describe a fork you can only rent, and moving off that vendor loses the performance you sized your pipelines around.
  • It is a distributed system with distributed overheads, and modern single-node tools such as DuckDB and Polars finish faster on datasets up to hundreds of gigabytes with no cluster to start, so a Spark job below that threshold is paying coordination cost for nothing.
  • Structured Streaming is micro-batch, which puts an end-to-end latency floor in the range of hundreds of milliseconds to seconds; workloads that need genuine per-event latency go to Flink instead, and discovering this after building on Spark means a rewrite.
  • Major upgrades deliberately break jobs: Spark 4.0 turns ANSI SQL mode on by default, so silent overflow and invalid casts that previously produced nulls now raise runtime errors, and a pipeline that worked for years can start failing purely on upgrade.
  • PySpark hides a process boundary, and Python UDFs serialise every row between the JVM and a Python worker; a direct translation of pandas code into PySpark UDFs can run an order of magnitude slower than the equivalent built-in expressions.

Sourcegraph

  • Enterprise pricing starting at $16k annually creates significant barrier for small teams
  • No free tier or standard plan published; all options require custom sales engagement
  • Complex enterprise-only positioning limits adoption compared to GitHub Copilot's pervasiveness
  • Requires indexing entire codebase; not suitable for small or ephemeral projects

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Sourcegraph

$16000/year
  • Enterprise$16000/year
    • Scales with team size
    • Credits for AI features per user
    • Deep Search

Which should you pick?

Choose Apache Spark if

  • You need unified engine.
  • You want to start without paying.
  • You also want catalyst optimiser.
Apache Spark review

Choose Sourcegraph if

  • You work on Cloud, Self-hosted.
Sourcegraph review

Questions people ask

Is Apache Spark or Sourcegraph better?
Neither clearly leads. Apache Spark starts at Free and Sourcegraph at $16000/year, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Sourcegraph?
Apache Spark has a free tier; the other does not. Paid plans start at Free for Apache Spark and $16000/year for Sourcegraph.
Does Apache Spark or Sourcegraph run on more platforms?
Apache Spark runs on Web. Sourcegraph runs on Cloud, Self-hosted.
Can I use Apache Spark for free?
Yes. Apache Spark has a free tier, so you can try it without paying. Sourcegraph starts at $16000/year.
What is Apache Spark best used for?
Apache Spark is most often used for nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows, building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction, feature engineering and model training across datasets too large to fit in pandas on one node, migrating legacy mapreduce or hive workloads onto an engine that is still actively developed and widely supported by cloud vendors. Of those, nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows and building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction are not what Sourcegraph is typically brought in for.
What can Apache Spark do that Sourcegraph cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming.

Answered from the vendors’ own pages

Apache Spark: When is Spark the wrong choice?

When your data fits comfortably on one machine. DuckDB or Polars will process hundreds of gigabytes on a single large node faster than a Spark cluster, without a scheduler, a driver or a shuffle. Spark earns its overhead when the data genuinely does not fit.

Apache Spark: Is Spark the same on Databricks as the open source version?

No. Databricks runs its own runtime including the proprietary Photon engine and its own optimisations, so performance figures and some behaviours do not carry over to open source Spark on EMR, Dataproc or your own Kubernetes cluster.

Apache Spark: Can I use Spark for real-time processing?

For near-real-time, yes, with Structured Streaming's micro-batch model, which lands in the sub-second to seconds range. For true per-event latency in the low milliseconds, Flink is the usual choice.

Apache Spark: Does upgrading between major versions break things?

Yes, by design in some cases. Spark 4.0 makes ANSI SQL mode the default, which converts previously silent overflow and cast failures into runtime errors. Upgrades need a testing pass over production pipelines rather than a version bump.

Apache Spark: Do I need to know Scala?

No. Python covers the vast majority of work and PySpark is the most common interface. Scala still helps when reading the source, writing custom data sources or diagnosing errors that surface as JVM stack traces.

Share

Related pages

More on Apache Spark

More on Sourcegraph

Keep looking

Other head to heads