fraiseql-wire

Streaming JSON queries for Postgres 17, built for FraiseQL

fraiseql-wire is a minimal, async Rust query engine that streams JSON data from Postgres with low latency and bounded memory usage.

It is not a general-purpose Postgres driver. It is a focused, purpose-built transport for JSON queries of the form:

SELECT data
FROM {source}
[WHERE predicate]
[ORDER BY expression [COLLATE collation] [ASC|DESC]]
[LIMIT N] [OFFSET M]

Where {source} is a JSON-shaped relation (v_{entity} views or tv_{entity} tables).

The primary goal is to enable efficient, backpressure-aware streaming of JSON from Postgres into Rust, with support for hybrid filtering (SQL + Rust predicates), adaptive chunking, pause/resume flow control, and comprehensive metrics.

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Why fraiseql-wire?

Traditional database drivers are optimized for flexibility and completeness. FraiseQL-Wire is optimized for:

• 🚀 Low latency (process rows as soon as they arrive)
• 🧠 Low memory usage (no full result buffering)
• 🔁 Streaming-first APIs (Stream<Item = Result<Value, _>>)
• 🧩 Hybrid filtering (SQL + Rust predicates)
• 🔍 JSON-native workloads

If your application primarily:

• Reads JSON (json / jsonb)
• Uses views as an abstraction layer
• Needs to process large result sets incrementally

…then fraiseql-wire is a good fit.

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Non-goals

fraiseql-wire intentionally does not support:

• Writes (INSERT, UPDATE, DELETE)
• Transactions
• Prepared statements
• Arbitrary SQL
• Multi-column result sets
• Full Postgres type decoding

If you need those features, use tokio-postgres or sqlx.

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Supported Query Shape

All queries must conform to:

SELECT data
FROM {source}
[WHERE <predicate>]
[ORDER BY <expression> [COLLATE <collation>] [ASC|DESC]]
[LIMIT <count>]
[OFFSET <count>]

Query Components

Component	Support	Notes
SELECT	SELECT data only	Result column must be named data and type json/jsonb
FROM	v_{entity} / tv_{entity}	Views and tables with JSON column
WHERE	SQL predicates	Optional; use where_sql() in builder
ORDER BY	Server-side sorting	With optional COLLATE; server-executed, no client buffering
LIMIT/OFFSET	Pagination	For result set reduction
Filtering	SQL + Rust predicates	Hybrid: SQL reduces wire traffic, Rust refines streamed data

Hard Constraints

• Exactly one column in result set (named data)
• Column type must be json or jsonb
• Results streamed in-order (server-side ordering for ORDER BY)
• One active query per connection
• No client-side reordering or aggregation

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Example

Streaming JSON results

use futures::StreamExt;

let client = FraiseClient::connect("postgres:///example").await?;

let mut stream = client
    .query("user")
    .where_sql("data->>'status' = 'active'")
    .chunk_size(256)
    .execute()
    .await?;

while let Some(item) = stream.next().await {
    let json = item?;
    println!("{json}");
}

Collecting (optional)

let users: Vec<serde_json::Value> =
    stream.collect::<Result<_, _>>()?;

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Hybrid Predicates (SQL + Rust)

Not all predicates belong in SQL. FraiseQL-Wire supports hybrid filtering:

let stream = client
    .query("user")
    .where_sql("data->>'type' = 'customer'")
    .where_rust(|json| expensive_check(json))
    .execute()
    .await?;

• SQL predicates reduce data sent over the wire
• Rust predicates allow expressive, application-level filtering
• Filtering happens while streaming

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Streaming Model

Under the hood:

• Results are read incrementally from the Postgres socket
• Rows are batched into small chunks
• Chunks are sent through a bounded async channel
• Consumers apply backpressure naturally via .await

This ensures:

• Bounded memory usage
• CPU and I/O overlap
• Fast time-to-first-row

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Cancellation & Drop Semantics

If the stream is dropped early:

• The in-flight query is cancelled
• The connection is closed
• Background tasks are terminated

This prevents runaway queries and resource leaks.

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Postgres 17 & Chunked Rows Mode

fraiseql-wire is designed to take advantage of Postgres 17 streaming behavior, and can optionally leverage chunked rows mode via a libpq-based backend.

The public API remains the same regardless of backend; chunking is an internal optimization.

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Quick Start

Installation

Add to Cargo.toml:

[dependencies]
fraiseql-wire = "0.1"
tokio = { version = "1", features = ["full"] }
futures = "0.3"
serde_json = "1"

Basic Usage

use fraiseql_wire::client::FraiseClient;
use futures::stream::StreamExt;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Connect to Postgres
    let client = FraiseClient::connect("postgres://localhost/mydb").await?;

    // Stream results
    let mut stream = client.query("users").execute().await?;

    while let Some(item) = stream.next().await {
        let json = item?;
        println!("{}", json);
    }

    Ok(())
}

Running Examples

See examples/ directory:

# Start Postgres with test data
docker-compose up -d

# Run examples
cargo run --example basic_query
cargo run --example filtering
cargo run --example ordering
cargo run --example streaming
cargo run --example error_handling

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Error Handling

Errors are surfaced as part of the stream:

Stream<Item = Result<serde_json::Value, FraiseError>>

Possible error sources include:

• Connection or authentication failures
• SQL execution errors
• Protocol violations
• Invalid result schema
• JSON decoding failures
• Query cancellation

Fatal errors terminate the stream.

For detailed error diagnosis, see troubleshooting.md.

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Performance Characteristics

• 📉 Memory usage scales with chunk_size, not result size
• ⏱ First rows are available immediately
• 🔄 Server I/O and client processing overlap
• 📦 JSON decoding is incremental

Benchmarked Performance (v0.1.0)

Memory Efficiency: The key advantage

Scenario	fraiseql-wire	tokio-postgres	Difference
10K rows	1.3 KB	2.6 MB	2000x
100K rows	1.3 KB	26 MB	20,000x
1M rows	1.3 KB	260 MB	200,000x

fraiseql-wire uses O(chunk_size) memory while traditional drivers use O(result_size).

Latency & Throughput: Comparable to tokio-postgres

Metric	fraiseql-wire	tokio-postgres
Connection setup	~250 ns (CPU)	~250 ns (CPU)
Query parsing	~5-30 µs	~5-30 µs
Throughput	100K-500K rows/sec	100K-500K rows/sec
Time-to-first-row	2-5 ms	2-5 ms

For detailed performance analysis, see performance-tuning.md and benches/comparison-guide.md.

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When to Use fraiseql-wire

Use this crate if you:

• Stream large JSON result sets
• Want predictable memory usage
• Use Postgres views as an API boundary
• Prefer async streams over materialized results
• Are building FraiseQL or similar query layers

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When Not to Use It

Avoid this crate if you need:

• Writes or transactions
• Arbitrary SQL
• Strong typing across many Postgres types
• Multi-query sessions
• Compatibility with existing ORMs

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Advanced Features

Type-Safe Deserialization

Stream results as custom structs instead of raw JSON:

#[derive(Deserialize)]
struct Project {
    id: String,
    name: String,
    status: String,
}

let stream = client.query::<Project>("projects").execute().await?;
while let Some(project) = stream.next().await {
    let p: Project = project?;
    println!("{}: {}", p.id, p.name);
}

Type T affects only deserialization; SQL, filtering, and ordering are identical regardless of T.

Stream Control (Pause/Resume)

Pause and resume streams for advanced flow control:

let mut stream = client.query("entities").execute().await?;

// Process some rows
while let Some(item) = stream.next().await {
    println!("{item?}");
    break;  // Stop after one
}

// Pause to do other work
stream.pause().await?;
// ... perform other operations ...
stream.resume().await?;  // Continue from where we left off

Adaptive Chunking

Automatic chunk size optimization based on channel occupancy:

let stream = client
    .query("large_table")
    .adaptive_chunking(true)    // Enabled by default
    .adaptive_min_size(16)      // Don't go below 16
    .adaptive_max_size(1024)    // Don't exceed 1024
    .execute()
    .await?;

SQL Field Projection

Reduce payload size via database-level field filtering:

let stream = client
    .query("users")
    .select_projection("jsonb_build_object('id', data->>'id', 'name', data->>'name')")
    .execute()
    .await?;
// Returns only id and name fields, reducing network overhead

Metrics & Tracing

Built-in metrics via the metrics crate:

• fraiseql_stream_rows_yielded – Total rows yielded from streams
• fraiseql_stream_rows_filtered – Rows filtered by predicates
• fraiseql_query_duration_ms – Query execution time
• fraiseql_memory_usage_bytes – Estimated memory consumption

Enable tracing with:

RUST_LOG=fraiseql_wire=debug cargo run

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Project Status

✅ Production Ready

• API is stable and well-tested
• 166+ unit tests, comprehensive integration tests
• Zero clippy warnings (strict -D warnings)
• Fully optimized streaming engine with proven performance characteristics
• Ready for production use

All core features implemented with comprehensive CI validation:

• ✅ Async JSON streaming (integration tests across PostgreSQL 15-18)
• ✅ Hybrid SQL + Rust predicates (25+ WHERE operators with full test coverage)
• ✅ Type-safe deserialization (generic streaming API with custom struct support)
• ✅ Stream pause/resume (backpressure-aware flow control)
• ✅ Adaptive chunking (automatic memory-aware chunk optimization)
• ✅ SQL field projection (SELECT clause optimization for reduced payload)
• ✅ Server-side ordering (ORDER BY with COLLATE support, no client buffering)
• ✅ Pagination (LIMIT/OFFSET for result set reduction)
• ✅ Metrics & tracing (comprehensive observability via metrics crate)
• ✅ Error handling (detailed error types and recovery patterns)
• ✅ Connection pooling support (documented integration patterns)
• ✅ TLS/SCRAM authentication (PostgreSQL 17+ security features)

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Roadmap

• Connection pooling integration guide
• Advanced filtering patterns
• PostgreSQL 15-18 compatibility
• SCRAM/TLS end-to-end integration tests in CI
• Comprehensive metrics and tracing
• Server-side ordering (ORDER BY with COLLATE)
• Pagination support (LIMIT/OFFSET)
• SQL field projection for payload optimization
• Extended metric examples and dashboards
• PostgreSQL 19+ compatibility tracking
• Binary protocol optimization (extended query protocol)

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Documentation

Document	Purpose
testing-guide.md	Running tests locally and in CI; release process
troubleshooting.md	Error diagnosis and common issues
performance-tuning.md	Tuning for production workloads
benchmarking.md	How to run and interpret benchmarks
advanced-filtering.md	Complex WHERE clause patterns
typed-streaming-guide.md	Type-safe streaming setup
connection-pooling.md	Pool configuration and tuning
metrics.md	All exposed Prometheus metrics
security-audit.md	Security assessment and findings
postgres-compatibility.md	PostgreSQL version compatibility
SECURITY.md	Security best practices and deployment hardening
docker-setup.md	Docker development environment setup
CONTRIBUTING.md	Development workflows and contribution guidelines
docs/operators.md	WHERE clause operator reference
docs/collation.md	Sorting and collation reference
benches/comparison-guide.md	Benchmark comparison guide
.github/publishing.md	crates.io publishing workflow

Examples

• examples/basic_query.rs – Simple streaming usage
• examples/filtering.rs – SQL and Rust predicates
• examples/ordering.rs – ORDER BY with collation
• examples/streaming.rs – Large result handling and chunk tuning
• examples/error_handling.rs – Error handling patterns

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Philosophy

This is not a Postgres driver. It is a JSON query pipe.

By narrowing scope, fraiseql-wire delivers performance and clarity that general-purpose drivers cannot.

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Credits

Author:

• Lionel Hamayon (@evoludigit)

Part of: FraiseQL — Compiled GraphQL for deterministic Postgres execution

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License

MIT OR Apache-2.0