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Your Fast Search Data Should Be Yours

· 2 min read
IndexTables Team
Core Maintainers

We're excited to launch the official IndexTables website — and to share our vision for open search in the lakehouse era.

The Search Revolution​

Six years ago, the data lakehouse revolution changed everything. Data stopped belonging to vendors. It started belonging to you.

But one domain missed the revolution: search. Observability and security search stacks are still dominated by closed, expensive ecosystems.

IndexTables brings that same open revolution to search — with performance that rivals the biggest proprietary platforms, built entirely on open tech.

Learn more: Why IndexTables

What is IndexTables?​

IndexTables brings high-performance full-text search to Apache Spark. Built on Tantivy and Quickwit, it runs embedded in Spark executors - no external servers required.

# Write with full-text indexing
df.write.format("io.indextables.spark.core.IndexTables4SparkTableProvider") \
.option("spark.indextables.indexing.typemap.content", "text") \
.save("s3://bucket/logs")

# Read and query with IndexQuery
logs = spark.read \
.format("io.indextables.spark.core.IndexTables4SparkTableProvider") \
.load("s3://bucket/logs")

logs.filter("content indexquery 'error AND database'").show()

Key Features​

  • No Infrastructure: Runs inside Spark executors - just add the library and start indexing
  • Spark Native: Full filter pushdown, aggregate pushdown, and partition pruning with Spark SQL
  • Cloud Ready: QuickwitSplit format optimized for S3 and Azure with L2 disk cache on NVMe
  • Full-Text Search: IndexQuery operators with Tantivy/Quickwit syntax - boolean queries, phrase search, fuzzy matching
  • 10-1000x Faster Analytics: Aggregations run directly in the search engine, not Spark
  • Time-Series Analytics: Built-in date histograms and bucket aggregations for log analysis

Getting Started​

Check out our Quickstart Guide to get up and running in 5 minutes.

Stay Connected​


It's your data. Your performance. Your choice.