Use the official PyExasol Python driver for direct Exasol connections, queries, bulk I/O, metadata, UDF logs, and multiprocessing. Covers pyexasol.connect, connect_local_config, ExaConnection, ExaStatement, fetch_mapper, convert_websocket_to_python, execute_sql_script, create_prepared_statement, import_from_*, export_to_*, insert_multi, .meta, sql_columns, explain_last, execute_udf_output, http_transport, exa_address, export_parallel, import_parallel, exasol.driver.websocket.dbapi2, and EXASOL_TELEMETRY_DISABLE.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this skill for Python code that talks directly to Exasol through
pyexasol. For version-specific behavior, check the requested release in the
official repository; do not mix APIs from
different releases.
Load only the matching references:
Connect, query, format SQL, fetch, or transact
pyexasol.connect, connect_local_config, ExaConnection, ExaStatement, execute_sql_script, create_prepared_statement, fetch_mapper, !i, !q, !rreferences/connections-and-sql.mdMove data with HTTP transport
export_to_, import_from_, pandas, Polars, Parquet, insert_multi, export_params, import_paramsreferences/bulk-transfer.mdInspect metadata or debug
.meta, sql_columns, list_tables, execute_udf_output, explain_last, debug_logdir, abort_queryreferences/metadata-and-debugging.mdRun parallel HTTP transfer
http_transport, export_parallel, import_parallel, get_nodes, exa_address, multiprocessing, threadsafetyreferences/parallel-http-transport.mdUse the PEP 249 interface
exasol.driver.websocket.dbapi2, DB-API 2.0, PEP 249, cursor, executemany, paramstyle, SQLAlchemyreferences/dbapi.mdLoad every reference that matches.
!r only
for a closed set of trusted SQL fragments.abort_query() is the
only supported cross-thread connection call.open_pyexasol_connection and other
notebook-connector wrappers.まだレビューはありません。使ってみた感想をお寄せください。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
まだコメントはありません。最初のコメントを書いてみませんか。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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).
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。