Performance upgrade preview

What is the performance upgrade preview?

InfluxDB 3 Enterprise includes a preview of major upgrades to the storage layer that improve how data is written, stored, compressed, compacted, and queried. These upgrades touch every layer of the storage path—from a new on-disk file format to how fields are organized into column families and how compaction manages resources.

Why these upgrades

The existing InfluxDB 3 storage layer uses Apache Parquet and is optimized for analytical workloads. Customers running high-cardinality, wide-schema, and query-intensive workloads need better single-series query performance, more predictable resource usage, and the schema flexibility that made InfluxDB v1 and v2 popular. These upgrades extend the storage layer to support those workloads while maintaining full compatibility with InfluxDB 3’s data model and query languages.

Key improvements include:

• Faster single-series queries: Single-digit millisecond response times for highly selective time-series queries.
• Consistent resource usage: Bounded CPU and memory during persistence and compaction, eliminating spikes during heavy ingestion or compaction bursts.
• Wide-and-sparse table support: Schemas with up to millions of columns and dynamic schema evolution without expensive rewrites.
• Column families: Group related fields for efficient compression and I/O, so queries only read the data they need.
• Bulk data export: Export compacted data as Parquet files for use with external tools.
• Automatic Parquet upgrade: Seamlessly migrate existing data with hybrid query mode during the transition.

Enable the preview

Include the --use-pacha-tree flag in your influxdb3 serve startup command:

influxdb3 serve \
  --node-id host01 \
  --cluster-id cluster01 \
  --object-store file \
  --data-dir ~/.influxdb3 \
  --use-pacha-tree

You can also enable the preview with an environment variable:

export INFLUXDB3_ENTERPRISE_USE_PACHA_TREE=true
influxdb3 serve ...

The --use-pacha-tree flag exposes additional configuration options prefixed with --pt-. See Configure the preview for tuning options, or Monitor the preview for system tables and telemetry.

What’s changed

These upgrades touch every layer of the storage path—from the on-disk file format to how data is compressed, organized, and compacted.

New file format

Data is stored in a new columnar file format (.pt files) optimized for time-series workloads. All data within a file is sorted by column family key, series key, and timestamp, which enables efficient compaction, querying, and filtering.

The format uses type-specific compression algorithms that adapt to data characteristics—delta-delta RLE for timestamps, Gorilla encoding for floats, dictionary encoding for low-cardinality strings, and more—typically achieving 5-20x compression ratios.

Column families

Column families let you group related fields together so that queries only read the data they need. Fields in the same family are stored together on disk. For wide tables with hundreds of fields, this dramatically reduces I/O.

When writing line protocol, use the :: (double-colon) delimiter in field names to assign fields to a family. The portion before :: is the family name; everything after is the field name.

metrics,host=sA cpu::usage_user=55.2,cpu::usage_sys=12.1,cpu::usage_idle=32.7 1000000000
metrics,host=sA mem::free=2048i,mem::used=6144i,mem::cached=1024i 1000000000
metrics,host=sA disk::read_bytes=50000i,disk::write_bytes=32000i 1000000000

This creates three column families:

When a query references only mem::free, the storage layer reads only the mem family block and skips cpu and disk data entirely.

Fields written without :: are assigned to auto-generated families (named __0, __1, etc.), each holding up to 100 fields. Explicit family names are an excellent way to optimize performance with known workloads, but they’re not required to achieve good results.

Bounded compaction

Incoming writes are buffered in the WAL, flushed to snapshots, and then merged into Gen0 files. The upgraded storage layer organizes compacted data into 24-hour UTC windows and progresses Gen0 files through four compaction levels (L1 through L4). Compaction runs continuously in the background with a byte-based memory budget (default: 50% of system RAM), so it never causes resource spikes.

Old files are cleaned up after a cooldown period, ensuring query replicas have time to see new checkpoints before old data is removed. Failures are automatically retried, and the system is designed to be self-healing for transient issues.

Upgrade from Parquet

Existing clusters with Parquet data can upgrade with zero manual migration. The upgrade is fully automatic and occurs on initial startup.

When you restart a cluster with --use-pacha-tree, the system:

1. Detects existing Parquet data and enters hybrid mode.
2. Clears the legacy WAL on ingest nodes and streams Parquet files through a conversion pipeline.
3. Integrates converted files into the new storage format through compaction.
4. Automatically transitions once all data is migrated.

During hybrid mode, queries merge results from both the legacy and upgraded storage layers. If there is a conflict (same series key and timestamp), the upgraded data takes precedence.

Monitor upgrade progress

Use system tables to track upgrade status:

-- Per-node upgrade status
SELECT * FROM system.upgrade_parquet_node

-- Per-file migration progress
SELECT * FROM system.upgrade_parquet

Configure upgrade behavior

Downgrade to Parquet

If you need to revert from the performance preview back to standard Parquet storage, use the influxdb3 downgrade-to-parquet command. This command updates the catalog and deletes all PachaTree-specific files from object storage.

Before you downgrade

1. Stop all nodes in the cluster before running the downgrade command. The command checks for running nodes and refuses to proceed if any are active.

   influxdb3 stop node --node-id <NODE_ID>

2. Verify table compatibility. The downgrade validates that all tables can be represented in Parquet format. Tables that exceed the Parquet column limit or contain columns without legacy Parquet column IDs block the downgrade.

Preview the downgrade

Use the --dry-run flag to list files that would be deleted without making any changes:

influxdb3 downgrade-to-parquet \
  --cluster-id cluster01 \
  --object-store file \
  --data-dir ~/.influxdb3 \
  --dry-run

Run the downgrade

influxdb3 downgrade-to-parquet \
  --cluster-id cluster01 \
  --object-store file \
  --data-dir ~/.influxdb3

After the downgrade completes, restart nodes without the --use-pacha-tree flag to resume standard Parquet storage mode.

For all available options, see Downgrade options.

Export to Parquet

You can export compacted data as Parquet files for use with external tools.

Export workflow

# Step 1: List available databases
influxdb3 export databases

# Step 2: List tables in a database
influxdb3 export tables -d mydb

# Step 3: List compacted 24-hour windows for a table
influxdb3 export windows -d mydb -t cpu

# Step 4: Export data as Parquet files
influxdb3 export data -d mydb -t cpu -o ./export_output

To export specific time windows only:

influxdb3 export data -d mydb -t cpu -w 2026-01-15,2026-01-16 -o ./export_output

Who should try the preview

Consider enabling the preview in your staging or development environment if you have workloads with:

• High cardinality or wide tables
• Frequent backfill across time ranges
• Query-heavy access patterns requiring low latency
• Sparse schemas with dynamic column creation
• Resource constraints where bounded memory and CPU usage matter

Bug reports and feedback

To share feedback on the performance upgrade preview:

• Contact InfluxData support
• Reach out to your InfluxData account team

Your feedback on stability and performance at scale helps shape the future of InfluxDB 3.