Arrow Flight Server & Trino Connector

Arrow Flight Server & Trino Connector

CQLite ships two components for querying Cassandra SSTables as Arrow data over the network:

• cqlite-flight — an Arrow Flight server that runs co-located with a single Cassandra node and exposes that node’s SSTables as queryable Arrow streams.
• cqlite-trino — a Trino connector that uses the Cassandra Sidecar to discover the ring, then fans out one Flight read per token range so a SELECT in Trino scans a whole cluster’s SSTables.

Together they turn a Cassandra cluster’s on-disk data into a distributed, analytics-friendly SQL surface without a read path through Cassandra’s own query engine.

This page is a user-facing distillation. The authoritative, exhaustive flag and property reference lives in the crate READMEs:

• cqlite-flight/README.md
• trino-connector/README.md
• docs/flight-trino/PLAN.md — full design context.

The Flight server and its compaction-on-read model

cqlite-flight runs co-located with a Cassandra node and reads that node’s SSTables directly off disk. On every read it performs an on-the-fly compaction merge — a k-way merge of the table’s SSTables that resolves last-write-wins and applies tombstones — then filters and streams the result as Arrow record batches. Key properties:

• Compaction on read. Each DoGet runs a full k-way merge of the table’s SSTables. The original SSTables are never modified or removed — the merge produces Arrow batches, not new files on disk.
• Reads flushed SSTables only. It reads data that Cassandra has already flushed to disk. Rows still in a memtable are invisible; run nodetool flush to make recent writes readable.
• Snapshot-aware. When a ticket names a Sidecar snapshot, the server reads that hard-linked, immutable snapshot directory instead of the live data dir — giving a stable file set even while Cassandra compacts underneath.
• SELECT parity. Rows are reconstructed with the same read path CQLite’s query engine uses, so the Arrow output matches what a Cassandra SELECT would return.

Because it reads on-disk files rather than issuing CQL, it adds no read load to Cassandra’s own request path. For a broader comparison of CQLite’s read surfaces and their freshness guarantees, see Read surfaces & freshness.

Run the published container

The server publishes as a multi-arch (linux/amd64 + linux/arm64) image to the GitHub Container Registry:

ghcr.io/pmcfadin/cqlite-flight

Once the package is public, pulling needs no authentication (no docker login):

docker pull ghcr.io/pmcfadin/cqlite-flight:latest   # or a vX.Y.Z release tag

The container ships no data of its own — it reads SSTables from a directory you mount at runtime. Run it co-located with a Cassandra node, mounting that node’s data dir read-only (:ro) and pointing --data-dir at it. It serves the Arrow Flight gRPC listener on :8815:

docker run --rm -p 8815:8815 \
  -v /var/lib/cassandra:/var/lib/cassandra:ro \
  -e RUST_LOG=info \
  ghcr.io/pmcfadin/cqlite-flight:latest \
  --data-dir /var/lib/cassandra/data --listen 0.0.0.0:8815

Notes:

• The read-only mount is recommended — the server never writes to the SSTables, it merges them on the fly into Arrow batches.
• Any directory laid out as <keyspace>/<table>[-<uuid>]/ works as --data-dir; it does not have to be a live node (a snapshot or exported SSTable tree works too).
• The image runs as a non-root user (uid 10001); ensure the mounted path is readable by that uid.

Flag	Default	Description
--data-dir	(required)	Root holding <keyspace>/<table>[-<uuid>]/ SSTable dirs.
--listen	0.0.0.0:8815	Flight gRPC listen address.
--batch-size	8192	Max rows per Arrow record batch.

See the server README for the full tag scheme, running from source, and building the image yourself.

The client ticket contract

Clients address a table with a JSON ticket. The server exposes a read-only Arrow Flight RPC surface:

RPC	Purpose
GetFlightInfo	Returns the Arrow schema (derived from the ticket DDL) plus an endpoint/ticket.
GetSchema	Returns just the Arrow schema for a ticket.
DoGet	Streams the merged, filtered table as Arrow record batches.
DoAction("table_stats") / ListActions	Returns per-table aggregate statistics (Σ live rows, Σ partition count, SSTable count) that the Trino connector uses to drive aggregation-pushdown planning.

The only supported action is table_stats; every other action type is unimplemented. All write/exchange RPCs (DoPut, DoExchange, Handshake, …) are likewise unimplemented — it is a read-only server.

The ticket is a small JSON document. Required fields name the table and carry the DDL used to build the schema for the merge; the rest are optional pushdowns:

{
  "keyspace": "ks",
  "table": "tbl",
  "ddl": "CREATE TABLE ks.tbl (...)",  // parsed into the schema for the merge
  "snapshot": "cqlite-abc",            // optional; null = live data dir
  "token_start": -3074457345618258602, // optional, exclusive  (range is (start, end])
  "token_end":   3074457345618258602,  // optional, inclusive
  "wraparound": false,                 // optional; true when token_start > token_end (range wraps the ring)
  "columns": ["pk", "v"],              // optional projection; null = all columns
  "predicates": [                      // optional; AND-combined, evaluated per row
    { "column": "v", "op": "Gt", "value": 10 }
  ]
}

• columns is a projection — only the listed columns are read and streamed. The Arrow schema reflects the projection.
• predicates are AND-combined and evaluated per row. op is one of Equal, In (value is a JSON array), Gt, Gte, Lt, Lte, Prefix. Operands are typed by the column’s CQL type.
• token_start / token_end filter on the token already stored on each partition key — no Murmur3 computation. This is how the Trino connector assigns a token range to each split. Set wraparound to true for a range that crosses the ring’s minimum token (token_start > token_end); it defaults to false.

PyArrow Flight example

import json
import pyarrow.flight as flight

client = flight.connect("grpc://localhost:8815")

ticket_json = json.dumps({
    "keyspace": "test_basic",
    "table": "simple_table",
    "ddl": "CREATE TABLE test_basic.simple_table (id uuid PRIMARY KEY, name text)",
    # optional pushdowns:
    # "columns": ["id", "name"],
    # "predicates": [{"column": "name", "op": "Prefix", "value": "a"}],
}).encode()

# Fetch just the Arrow schema:
info = client.get_flight_info(flight.FlightDescriptor.for_command(ticket_json))
print(info.schema)

# Stream the merged, filtered table:
reader = client.do_get(flight.Ticket(ticket_json))
table = reader.read_all()
print(table.to_pandas())

uuid / timeuuid columns carry the Arrow UUID extension metadata. See the ticket contract in the README for the complete field reference.

The Trino connector

cqlite-trino is a Trino plugin that turns the single-node Flight server into a cluster-wide SQL surface. A SELECT over cqlite.<keyspace>.<table> streams compaction-merged, token-range-deduped Arrow data back to Trino.

Discovery and the split model

The connector discovers nodes and token ranges through the Cassandra Sidecar (ring, token-range-to-replica mapping, and schema). It then builds one split per token range, pinned to a single replica — so each row is read exactly once cluster-wide, with no duplication across replicas. Each split issues a DoGet to its replica’s cqlite-flight endpoint with that token range in the ticket.

The split count follows the Sidecar’s token-range response for the ring — roughly nodes × vnodes_per_node (e.g. a single node with Cassandra’s default 16 vnodes yields 16 splits; an N-node vnode ring scales up accordingly).

Type support: scalar columns (int, bigint, text, boolean, uuid, timestamp, …) and collections/tuples/UDTs (issue #2815): list<E>/set<E> → array(E), tuple/UDT → row(f1 A, …) (field names + order preserved), map<K,V> → map(K, V), mapped recursively (so list<list<text>> → array(array(varchar))). Element/field/key/value types are limited to the connector’s scalar leaf set; a collection whose leaf is an unsupported type (decimal, varint, sub-millisecond timestamp) is still hidden. A list<frozen<udt>> projects as array(varchar) whose elements are the server-decoded UDT strings — fully-typed UDT rows are tracked in issue #2349. A column whose type still cannot be mapped is hidden from the schema with a warning emitted on every projection (never silently dropped); a fully-unsupported table fails with a clear NOT_SUPPORTED error.

Install the plugin

A Trino plugin is not a single jar — Trino loads each plugin from an isolated classloader directory, so the connector jar must sit alongside all of its runtime dependencies. You assemble that directory of jars and drop it into Trino’s plugin path (/usr/lib/trino/plugin/cqlite_flight).

From this repo (the Gradle wrapper lives in trino-connector/):

cd trino-connector && ./gradlew installPlugin   # produces build/plugin/cqlite_flight/

From the published Maven Central artifact (in.mcfad:cqlite-trino:<version>), assemble the directory with a throwaway Gradle project that copies the resolved runtime classpath:

build.gradle.kts

plugins { java }
repositories { mavenCentral() }
dependencies { implementation("in.mcfad:cqlite-trino:<version>") }
tasks.register<Sync>("assemblePlugin") {
    into(layout.buildDirectory.dir("plugin/cqlite_flight"))
    from(configurations.runtimeClasspath)
}

./gradlew assemblePlugin yields build/plugin/cqlite_flight/ (connector jar + flight-core + jackson + transitive deps). trino-spi is intentionally not a runtime dependency — Trino supplies it from the engine classpath.

Required JVM configuration (Arrow off-heap, JDK 17+)

The connector reads Flight batches into off-heap Apache Arrow buffers. On JDK 17+ arrow-java’s MemoryUtil initializer needs the java.nio module opened to it, so Trino’s jvm.config must contain this exact line:

--add-opens=java.base/java.nio=org.apache.arrow.memory.core,ALL-UNNAMED

Trino 481’s stock jvm.config does not include it. Without the flag the first Arrow off-heap touch fails in MemoryUtil.<clinit>, and every do_get then dies downstream with an opaque Failed to read message. Add the flag to every coordinator and worker, then restart.

The connector fails fast to make this actionable: it probes Arrow memory initialization once at catalog load and, if the flag is missing, raises a Trino CONFIGURATION_INVALID error naming this exact flag and the jvm.config remedy — rather than surfacing the cryptic read-time error. The bundled docker stack and the k8s trino-cqlite kit both configure the flag for you.

Catalog configuration and read mode

Configure a catalog in etc/catalog/cqlite.properties. The first line must select the connector factory by name; the rest are the cqlite.* properties:

etc/catalog/cqlite.properties

connector.name=cqlite_flight
cqlite.sidecar-uri=http://cassandra-sidecar:9043
# cqlite.read-mode=snapshot   # optional; snapshot is the default

The key properties:

Property	Default	Description
connector.name	(required)	Must be cqlite_flight — selects this connector’s factory so Trino can load the catalog.
cqlite.sidecar-uri	(required)	Cassandra Sidecar base URI for DDL / ring discovery.
cqlite.flight-port	8815	Arrow Flight port on each Cassandra node.
cqlite.local-datacenter	(none)	Preferred datacenter for split placement.
cqlite.read-mode	snapshot	See below.

The cqlite.read-mode property picks which file set a scan sees, since Cassandra compacts and flushes continuously underneath:

Mode	File set	Consistency tradeoff
snapshot (default)	A Cassandra Sidecar snapshot — a hard-linked, immutable copy of the SSTables taken at planning time.	Stable file set, bounded staleness. Every split reads the same immutable files; data is “as of” snapshot time, with no mid-scan file churn. The safe default.
live	The node’s current data directory.	Most current, but races compaction. A long scan can have files compacted or removed under it. Use when you want the absolute latest flushed data and accept the race.

In snapshot mode the connector creates a per-query snapshot (cqlite-<queryId>) at planning time, names it in every ticket, and best-effort deletes it when the query finishes; a TTL backstop (cqlite.snapshot-ttl, default 6h) ensures a coordinator crash can’t leak it. Snapshot creation is fail-closed — if it fails, the query fails rather than silently falling back to a live read.

To cut snapshot-create (and therefore memtable-flush) churn, the connector reuses one snapshot per (keyspace, table) across queries within a bounded staleness window (cqlite.snapshot-reuse-window-ms, default 3000), retiring superseded windows after cqlite.snapshot-retire-grace-ms (default 10m, must exceed your longest query). See Snapshot reuse and the staleness bound in the connector README for the sizing rules (issue #2356/#2306).

Memtable visibility differs by mode (issue #2305). snapshot mode is a per-query, point-in-time read that includes durable memtable state: Cassandra’s snapshot creation flushes the memtable as a side effect by design, and the Sidecar’s create-snapshot HTTP API has no skipFlush parameter to opt out of it. So a very recent write can become visible through a snapshot-mode query even without an explicit nodetool flush. live mode is the strict on-disk-SSTables-only contract: it reads the current data directory directly, with no snapshot and no flush side effect, so memtable rows stay invisible until an explicit nodetool flush. See the connector README for the full mechanism.

See the connector README for the complete property reference, the docker-compose end-to-end topology, LIMIT / aggregation pushdown behavior, and version-skew notes.

Where this fits

The Flight server + Trino connector are one of several CQLite read surfaces. For building a Parquet lakehouse projection triggered by SSTable flush events instead, see Lakehouse projections with the Cassandra Sidecar.