Exasol User Defined Functions (UDFs) and Script Language Containers (SLCs). Covers `CREATE SCRIPT`, SCALAR and SET functions, variadic scripts with `EMITS(...)` and `default_output_columns`, the `ExaIterator` and `ExaMetadata` APIs, Python, Java, Lua, and R scripts, Lua execute scripts and `pquery`, BucketFS file access, GPU-accelerated UDFs, `ALTER SESSION SET SCRIPT_LANGUAGES` activation, and building custom Script Language Containers with `exaslct`, `exaslpm`, and `packages.yml`.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
UDFs extend SQL with custom logic that runs inside the Exasol cluster: per-row transforms, custom aggregation, ML inference against a model in BucketFS, external API calls, and DataFrame batch processing. They execute in a Script Language Container — a Docker-based runtime whose contents you can replace when the default packages are not enough.
SCALAR or SET?
| SCALAR | SET | |
|---|---|---|
| Input | One row at a time | Group of rows (via GROUP BY) |
| Output | RETURNS <type> (single value) | EMITS (col1 TYPE, ...) (zero or more rows) |
| Row iteration | Not needed | ctx.next() loop required |
| SQL usage | SELECT udf(col) FROM t | SELECT udf(col) FROM t GROUP BY key |
| Use case | Per-row transforms | Aggregation, ML batch predict, multi-row emit |
Which language?
| Language | Startup | Best For | Expandable via SLC? |
|---|---|---|---|
| Python 3 (3.10 or 3.12) | ~200ms | ML, data science, pandas, string processing | Yes |
| Java (11 or 17) | ~1s | Enterprise libs, type safety, Virtual Schema adapters | Yes |
| Lua 5.4 | <10ms | Low-latency transforms, row-level security | No (natively compiled into Exasol) |
| R (4.4) | ~200ms | Statistical modeling, R model deployment | Yes |
Choose the narrowest matching route. Several often apply — a Python UDF that reads a model needs routes 1 and 2 — so load all matching references.
Write the CREATE SCRIPT statement — syntax for any language, variadic scripts
CREATE SCRIPT, SCALAR, SET EMITS, RETURNS, variadic script, dynamic parameters, EMITS(...), default_output_columns, %scriptclass, %jar, R UDF, Lua UDFreferences/create-script-syntax.mdPython UDF internals — context API, type mapping, DataFrames, BucketFS reads, testing
Python UDF, ctx.emit, ctx.next, get_dataframe, pandas, pickle, load model, udf-mock-python, dynamic import, memory limitreferences/udf-python.mdJava or Lua UDF internals — ExaIterator, ExaMetadata, JARs, JVM options, adapters, Lua libraries
Java UDF, ExaIterator, ExaMetadata, getString, %jar, JVM options, script imports, ADAPTER script, remote debugging, Lua contextreferences/udf-java-lua.mdBuild or deploy a Script Language Container — flavors, packages, activation, troubleshooting
SLC, Script Language Container, exaslct, exaslpm, packages.yml, custom packages, flavor, conda, CUDA, GPU UDF, SCRIPT_LANGUAGES, ALTER SESSION, ALTER SYSTEM, security-scanreferences/slc-reference.mdOrchestrate SQL from inside the database — not a UDF
execute script, Lua execute, pquery, in-database orchestration, iterative algorithm, multi-step SQL workflowreferences/lua-execute-scripts.mdRoute 5 is a genuine fork, not a variant of the others: use a Lua execute script rather than a UDF when the task is to orchestrate multi-step or iterative SQL workflows from within the database.
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Top-level router for Exasol work. Use for any Exasol database, exapump, SQL, BucketFS, extension, integration, UDF, Script Language Container, or Exasol Personal setup task, then route to the narrowest specialized Exasol skill.
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Exasol BucketFS file system management via the `exapump bucketfs` CLI. Covers listing, uploading, downloading, and deleting files and directories in BucketFS, the `bfsdefault` service and other bucket services, bucket structure, `bfs_*` profile settings, the `/buckets/<service>/<bucket>/<path>` UDF path, and staging JARs, models, and Script Language Containers for UDFs.
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Use Exasol Cloud Storage Extension workflows for moving structured object-storage files through extension UDFs. Covers setup prerequisites, the fixed `CLOUD_STORAGE_EXTENSION.IMPORT_PATH` and `CLOUD_STORAGE_EXTENSION.EXPORT_PATH` entrypoints used with `FROM SCRIPT` and `INTO SCRIPT`, Parquet, Avro, ORC, and Delta files on S3, Google Cloud Storage, Azure Blob Storage, Azure Data Lake, HDFS, and Alluxio, connection-object credential patterns, and routing away from native IMPORT or EXPORT when the extension path is the right fit.
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Exasol database interaction via the exapump CLI and Exasol SQL. Covers SQL queries and DML/DDL such as `SELECT`, `INSERT`, `UPDATE`, `DELETE`, `MERGE`, `CREATE TABLE`, `ALTER TABLE`, and `DROP TABLE`, `CREATE CONNECTION` connection objects without import or export intent, schema inspection, table design, `exapump sql` and `exapump profile`, query profiling, analytics, and Exasol-specific SQL behavior outside exasol-import and exasol-export.
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Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).
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Use Exasol document-file virtual schemas for federated read-only access to object and file storage — S3, Google Cloud Storage, Azure Blob Storage, and Azure Data Lake Storage Gen2 document files. Covers document-file adapter family selection, the EDML `MAPPING` definition, `CREATE VIRTUAL SCHEMA` usage, refresh workflows, and query-side troubleshooting.
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