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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.

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