Apache Iceberg’s REST Catalog protocol gives clients a common interface to catalog operations; it does not make two clients equally fast. Their elapsed time can differ because of client implementation, catalog features, metadata and cache state, scan-planning mode, engine settings, network round trips, and the data scan itself. To find the cause, measure query startup and execution as separate phases rather than blaming the protocol.
What does one Iceberg REST protocol standardize?
The REST Catalog protocol is intended to let a compliant client communicate with a compliant catalog server. As the Apache Iceberg REST Catalog Protocol documentation puts it, “a single client implementation works with any compliant server.” That is an interoperability goal, not a promise that clients perform the same work or return results with equal latency.
Clients can differ in which optional features they support or use, how they cache metadata, and where they plan scans. Servers can also expose different capabilities. A wall-clock comparison therefore needs to distinguish protocol calls from client-side processing, server work, engine planning, and data reading.
Why is Iceberg query planning slow?
Planning includes more than asking the catalog for a table. A useful investigation separates catalog setup and calls, table metadata loading and parsing, scan planning, engine optimization, execution and data scanning, and result delivery. Not every client exposes a timer for each phase; this is a measurement framework based on the documented request and planning lifecycle, not a set of standard client metrics.
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Catalog setup and network round trips
A REST client discovers server configuration at initialization with GET /v1/config. The response can provide defaults, enforce overrides, and advertise optional endpoints. Differences in client implementations or server capabilities can change the effective configuration and feature path. When setup or catalog latency is high, count the requests and record the advertised endpoints and effective settings.
Table loading, metadata, and cache state
Loading a table ordinarily requires its metadata. The REST protocol documents ETag-aware loading: a client can send If-None-Match and reuse cached table metadata when the server responds with 304 Not Modified. It also documents lazy snapshot loading, which can avoid retrieving full snapshot history when only branch and tag references are needed.
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These behaviors make cold and warm starts different cases. Record whether the client’s metadata cache was populated, what metadata it fetched, and the table’s metadata state; otherwise a cache hit for one client may be mistaken for an inherent protocol or implementation advantage.
Manifest pruning and scan planning
Iceberg’s performance documentation describes how the manifest list stores partition-value ranges for manifests, while manifests contain data-file partition information and column statistics. Planning can prune manifests and then rule out files that cannot match a query predicate. That can cut planning work and later I/O, but the benefit depends on the metadata, predicate, and table layout; it is not a fixed speed multiplier. See the Iceberg 1.9.0 performance documentation.
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With client-side planning, the client reads metadata and forms file scan tasks locally. With optional server-side planning, it sends the filter, snapshot, and selected columns to the server, which returns tasks and may use its own caches or indexes. The documented lifecycle may involve submitting a plan, polling with a plan ID, and fetching task batches. Server-side planning can reduce metadata downloads, but adds server work and may add polling and network wait.
The documented Java REST client defaults to client planning mode. Server-side planning is available only when the server advertises support. Check the actual client mode and server capabilities rather than assuming that two clients use the same planning path. The relevant details are in the REST Catalog Protocol documentation.
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Engine planning, execution, and result delivery
Having file tasks does not mean the query is done planning: the query engine may still optimize the plan, and execution must read the data and deliver results. For example, the Trino 483 Iceberg connector documentation covers statistics for cost-based optimization, metadata caching, split sizing, and other connector settings. Those are engine-level factors, not effects of the REST protocol alone. Because defaults and capabilities can change, verify them for the deployed Trino version and configuration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does an Iceberg REST catalog improve query performance?
Not by itself in every workload. REST standardizes catalog access; performance depends on how much catalog and metadata work the query needs, where planning occurs, what the engine does, and how much data must be scanned. Server-side planning may save metadata transfer in one situation while increasing server work or network waiting in another. Metadata pruning may eliminate files, but its impact depends on whether the table’s metadata and the query predicate make that pruning effective.
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Existing table-format benchmarks do not answer which of two Iceberg REST clients is faster. The CIDR 2023 paper Analyzing and Comparing Lakehouse Storage Systems reported that, in its 3 TB TPC-DS experiment, runtime was 1.4× faster on Delta than Hudi and 1.7× faster on Delta than Iceberg. The paper attributes observed differences to factors including reading time, file sizes and counts, a custom Parquet reader, and query-plan differences. Those results compare formats and implementations in a particular Spark setup, not two Iceberg clients against one REST server.
The paper also observes that metadata operations can become a planning bottleneck for very small queries and describes plan caching in the Hudi system used in that experiment. That is a reason to measure metadata and startup separately, not evidence that one Iceberg client is universally faster. Apache Hudi’s 2026 project-authored article, “Apache Hudi vs Apache Iceberg Performance: What Benchmarks Show, and What We Measured”, likewise emphasizes workload shape, configuration parity, and tested versions; it presents a project perspective, not an apples-to-apples ranking of Iceberg REST clients.
How do I compare two Iceberg clients?
Use the same catalog and workload, then identify which phase accounts for the difference. A credible comparison should keep the server, table state, query, and resources constant; measure cold and warm cache behavior separately; and report repeated results rather than one run.
- Fix the comparison conditions. Use the same catalog server and configuration, table snapshot and metadata state, query text and parameters, storage and network region, client/engine resource limits, and concurrency.
- Record versions and capabilities. Capture each client and engine version, the server version where available, the advertised REST endpoints, effective configuration, and whether scan planning is client-side or server-side.
- Run cold-cache and warm-cache cases. Keep those results distinct so metadata-cache hits do not obscure the cost of initial loading.
- Instrument the lifecycle. Where instrumentation permits, record catalog request count and latency, metadata bytes fetched and parsing time, planning duration, plan-task turnaround, engine planning, execution, data bytes and files scanned, and result-transfer time. Separate startup/planning from execution and result delivery.
- Repeat and report distributions. Run multiple trials and report a median and a tail-latency measure, along with workload and configuration details. Do not present a single run as a general client ranking.
For each genuine alternative, compare these dimensions rather than only total elapsed time:
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- Catalog round trips and REST feature support.
- Metadata fetched and cache behavior.
- Client-side versus server-side planning, including plan-task turnaround.
- Engine planning and use of statistics.
- Files and bytes scanned, plus end-to-end latency.
- Operational cost and server-side requirements.
Feature availability is release-specific, so verify it for the client and server versions in use. A direct, published apples-to-apples benchmark of two Iceberg clients on the same REST server and workload is not established by the cited evidence; no defensible universal winner follows from the format-level results above.
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