[836] xtable-spark-runtime: thin drop-in Spark bundle for 0.4.x (Spark 3.4 + 3.5)#843
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vinishjail97 merged 13 commits intoJul 21, 2026
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Ports the xtable-spark-runtime packaging slice onto the Hudi 0.x line
(main-hudi-0x, Hudi 0.14 / Spark 3.4). New thin, relocated bundle that
runs an incremental XTable sync inside a Spark job; engines are provided
by the cluster, never bundled.
Module:
- xtable-spark-runtime_${scala.binary.version}: xtable-core compile;
Spark/Hadoop and the engines (Hudi/Iceberg/Delta) provided. Curated
shade allowlist (xtable modules + guava/protobuf/commons-cli relocated);
avro/parquet/jackson NOT relocated (exchanged with the engines). Thin
~3.7 MB bundle.
- XTableSparkSync: standalone spark-submit entry point (Apache Commons CLI).
- XTableSyncService / TableSyncSpec: build an INCREMENTAL ConversionConfig
and run ConversionController.sync; target metadata path = source data
path (required by Hudi; Iceberg data lives under <basePath>/data).
Hudi 0.14 specifics (vs the Hudi 1.x variant on main):
- No hudi-hadoop-common (that split is 1.x); hudi-common provides FSUtils.
- Engine classpath uses the hudi-spark bundle (its regenerated Avro model
classes link on Avro 1.12, which Iceberg 1.9.2 requires) plus
hudi-java-client for the Hudi target's Java write client, with the raw
hudi-common excluded so the bundle's clean DecimalWrapper wins.
ConversionTargetFactory: make ServiceLoader discovery resilient so a
subset of engines works when others are absent (warn + skip on
LinkageError/ServiceConfigurationError; name-based Delta-Kernel check).
ITXTableSparkRuntimeBundle: spark-submits the shaded jar for one case per
direction across Hudi, Iceberg and Delta (source and target), engines on
a flat classpath, asserting data-equivalence over comparable scalar
columns. Requires a Spark 3.4 SPARK_HOME; skipped otherwise.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
vinishjail97
commented
Jul 10, 2026
…sing, RFC) Publish the shaded jar as the MAIN artifact with a dependency-reduced POM (shadedArtifactAttached=false, createDependencyReducedPom=true), matching iceberg-spark-runtime / hudi-spark-bundle. Resolving the coordinate via a Maven dependency or --packages now yields the relocated bundle and pulls no un-relocated transitive deps; --jars is equivalent. The thin main jar + separate -bundle classifier (which re-introduced the cluster guava clash) is gone. IT findBundleJar() now picks the shaded main jar. Pass release/scripts/validate_shaded_license_coverage.sh (the existing allowlist gate): the shade <includes> must equal the runtime dependency tree, so jackson / scala-library / log4j-1.2-api - which every Spark runtime supplies - are declared provided (dropped from the tree, kept off the shaded jar) and excluded from the IT's flat engine classpath so Spark's own copies win. avro/parquet stay on the flat classpath (the engine's newer avro must win over Spark 3.4's). Bundled-dependency licensing: add META-INF/LICENSE-bundled and NOTICE-bundled (wired via IncludeResourceTransformer) attributing the only bundled third-party - guava's closure (Apache-2.0) and commons-cli (Apache-2.0), plus checker-qual (MIT). Remove the dead protobuf-java allowlist entry and relocation: protobuf resolves as provided and was never bundled. RFC-3: scope v1 to the CLI (XTableSparkSync); mark the config-only XTableSyncListener on-ramp (and XTableSparkConfig / PlanTargetResolver) as a deferred follow-up. Fix the activation example to the shipped CLI and document the required engine Avro version + flat-classpath placement. XTableSyncService: normalize sourceFormat with Locale.ROOT. Root pom: exclude the shade-generated dependency-reduced-pom.xml from spotless (no license header; CI runs clean install). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Consolidate repeated rationale in the bundle pom (no functional change): - Tell the hudi-common DecimalWrapper / Avro-1.8-1.9 story once (on the hudi-spark bundle dep); the hudi-java-client exclusion just points to it. - State "engine Avro must win on a flat classpath" once (engine-classpath plugin); the avro dep and excludeGroupIds comments reference it. - Explain jackson/scala-library are Spark-supplied once. - Fix a stale "Spark 3.5" reference to Spark 3.4 (this is the Hudi 0.x line). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add main-hudi-0x to the push and pull_request branch filters of both workflows (same change as apache#841) so CI runs on PRs targeting the Hudi 0.x release line, including this one. A pull_request workflow's branch filter takes effect from the PR's own branch, so this makes CI fire on apache#843. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
vinishjail97
marked this pull request as ready for review
July 10, 2026 01:29
…k-submit stdout off-thread - ConversionTargetFactory: hasNext() resolves provider classes lazily and can throw ServiceConfigurationError too, so move it inside the existing ServiceConfigurationError|LinkageError guard alongside next(). - ITXTableSparkRuntimeBundle: drain spark-submit stdout on a background thread so a hung process is caught by waitFor(10min) instead of blocking forever on readOutput() reading until EOF.
main-hudi-0x was deleted and replaced by branch-0.4 (the 0.4.0 release line), so update the push/pull_request branch filters accordingly.
…istro ITXTableSparkRuntimeBundle spark-submits the shaded bundle jar and self-skips unless SPARK_HOME is set, so the main CI never runs it. Add a path-filtered workflow that installs a matching Spark 3.4 distribution from the Apache archive, sets SPARK_HOME, and runs the failsafe IT.
…filter) The workflow is intended to be a required status check on branch-0.4. A required check whose workflow is skipped by a path filter never reports, leaving PRs blocked on a pending check, so run it unconditionally on the covered branches. The Spark distro is cached, so the added cost is the reactor build plus the ~20s IT.
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Thanks @vinishjail97 ~ I think this will make life 10x better for users.
…unix flags, docs) - XTableSparkSync: validate --sourceformat/--targets up front and fail fast (before SparkSession creation) on empty or unsupported values. Split the allowed sets: sources are Hudi/Iceberg/Delta/Paimon/Parquet, targets are Hudi/Iceberg/Delta (Paimon/Parquet are read-only, no ConversionTarget). - XTableSyncService.sourceProviderFor: wire Paimon and Parquet source providers (previously threw UnsupportedOperationException); engines remain cluster-provided. - Rename CLI long-opts to unix-style lowercase (--basepath, --sourceformat, --tablename, --datapath, --partitionspec); update javadoc, IT and RFC example. - basePathToName -> basePathToTableName: handle "/", trailing slashes and null by throwing with a "pass --tablename" hint instead of an empty table name. - Add XTableSparkSyncTest covering table-name derivation and source/target format validation. - ConversionTargetFactory: log the skipped provider's error class/message so operators can distinguish an intentionally-absent engine from a linkage error. - spark-runtime-validation.yml: add a workflow_dispatch spark_version input and document why the Spark 3.4 line is pinned (Delta 2.4.0 is Spark-3.4-only). - RFC-3: add a supported-formats/engine-versions section, "from application code" (Scala/Java + PySpark) activation examples, and list @vinothchandar as an approver.
vinishjail97
changed the title
Delta source/target now auto-switch from delta-core to the Spark-free Delta Kernel implementation on Spark 3.5+ (where the bundled delta-core does not run), controlled by a single --usedeltakernel toggle that is auto-enabled by Spark version. Hudi/Iceberg sync was already Spark-free. Also runs the spark-runtime bundle IT on both Spark 3.4.3 and 3.5.9 via a CI matrix. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
vinishjail97
changed the title
Both workflows used a job named "build", so both reported the same status-check context and could not be required distinctly. Set unique job names so branch protection (see apache#848) can require each on branch-0.4. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
vinothchandar
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Jul 20, 2026
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Thanks for addressing the CR comments. Good to land once CI is green
XTableSparkSync accepts a --datasetconfig YAML (sourceFormat, targetFormats, datasets[]) to sync multiple tables in one run, mutually exclusive with the single-table --basepath/--sourceformat/--targets flags. The config is read through the Spark Hadoop config, so it may live on a local or cloud (s3/gcs/abfs) path, and reuses the same schema as the RunSync utility. Parsed with SnakeYAML's SafeConstructor (plain maps/lists only, no arbitrary type instantiation). SnakeYAML is bundled and relocated to org.apache.xtable.shaded (like guava/commons-cli) because Spark ships its own version (1.33 on 3.4, 2.0 on 3.5) that would otherwise clash; its multi-release classes are filtered out so no un-relocated org.yaml classes remain. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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In addition to the Setup:
Result (each run):
Module docs (README plus a Spark quickstart, including a |
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Ports
xtable-spark-runtimeonto the Hudi 0.x line (branch-0.4, Hudi 0.14 / Spark 3.4) — a thin, relocated Spark bundle that runs an incremental XTable sync in-job; engines are provided by the cluster, never bundled. Hudi-0.x counterpart of #838; part of the 0.4.0 on-ramp.xtable-spark-runtime(~3.7 MB) withXTableSparkSyncspark-submitentry point; resilientConversionTargetFactoryServiceLoader discovery so a subset of engines works when others are absent.hudi-hadoop-common(1.x split); engine classpath uses thehudi-sparkbundle so Avro model classes link on Avro 1.12 (Iceberg 1.9.2), plushudi-java-client.delta-coreon Spark 3.4 and auto-switches to the Spark-free Delta Kernel on Spark 3.5+ (where the bundleddelta-coredoes not run). A single--usedeltakernelflag forces Kernel on any version; otherwise it is auto-enabled by Spark version.ITXTableSparkRuntimeBundlecovers each direction across Hudi/Iceberg/Delta,spark-submit-ing the shaded jar and asserting the target is data-equivalent to the source. It'sSPARK_HOME-gated, so the Spark Runtime Bundle Validation workflow runs it on a matrix of Spark 3.4.3 and 3.5.9 (green); the standard CI build skips it.main, to merge after 0.4.0 once the module lands there).