CakePHP Cookbook 4.xChanges Installation using Oven Permissions Development Server Production Fire It Up URL Rewriting Apache nginx NGINX Unit IIS7 (Windows hosts) Lighttpd I Can’t Use URL Rewriting Configuration Configuring TIME_START SECOND MINUTE HOUR DAY WEEK MONTH YEAR Chronos Debug Kit Migrations ElasticSearch Appendices 4.x Migration Guide 4.0 Migration Guide 4.1 Migration Guide 4.2 Migration Guide 4.3 Migration Guide Fixture application already has its dependencies updated to 4.x or PHPUnit8. # Install the upgrade tool git clone https://github.com/cakephp/upgrade cd upgrade git checkout 4.x composer install --no-dev With the upgrade0 码力 | 1249 页 | 1.04 MB | 1 年前3
Jupyter Notebook 4.x Documentationexceeds this value. Session.item_threshold : Int Default: 64 The maximum number of items for a container to be introspected for custom serialization. Containers larger than this are pickled outright. %%javascript var CellToolbar = Jupyter.CellToolbar var toggle = function(div, cell) { var button_container = $(div) // let's create a button that shows the current value of the metadata var button button.text(String(!v)); }) // add the button to the DOM div. button_container.append(button); } // now we register the callback under the name foo to give the // user the ability0 码力 | 128 页 | 1.86 MB | 1 年前3
Krita 4.x 官方文档中文版 2021-08-06Aand run the container Enter the container and build Krita Building AppImage package for your version of Krita Creating a full clone of the container Testing merge requests using container clones Updating Updating dependencies in the docker Extra developer tools Stopping the container and cleaning up Troubleshooting Krita binary is not found after the first build OpenGL doesn’t work on NVidia GPU with proprietary are not included into the Dockerfile to save internal bandwidth # create directory structure for container control directory git clone https://invent.kde.org/dkazakov/krita-docker-env krita-auto-1 cd krita-auto-10 码力 | 1373 页 | 74.74 MB | 1 年前3
Apache Kyuubi 1.7.0 DocumentationWelcome Apache Kyuubi™ is a distributed and multi-tenant gateway to provide serverless SQL on Data Warehouses and Lakehouses. Kyuubi builds distributed SQL query engines on top of various kinds of modern modern computing frameworks, e.g., Apache Spark [https://spark.apache.org/], Flink [https://flink.apache.org/], Doris [https://doris.apache.org/], Hive [https://hive.apache.org/], and Trino [https://trino to build a modern data stack. For example, you can use Kyuubi, Spark and Iceberg [https://iceberg.apache.org/] to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and online0 码力 | 400 页 | 5.25 MB | 1 年前3
Apache Kyuubi 1.7.2 DocumentationWelcome Apache Kyuubi™ is a distributed and multi-tenant gateway to provide serverless SQL on Data Warehouses and Lakehouses. Kyuubi builds distributed SQL query engines on top of various kinds of modern modern computing frameworks, e.g., Apache Spark [https://spark.apache.org/], Flink [https://flink.apache.org/], Doris [https://doris.apache.org/], Hive [https://hive.apache.org/], and Trino [https://trino to build a modern data stack. For example, you can use Kyuubi, Spark and Iceberg [https://iceberg.apache.org/] to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and online0 码力 | 405 页 | 5.26 MB | 1 年前3
Apache Kyuubi 1.7.3 DocumentationWelcome Apache Kyuubi™ is a distributed and multi-tenant gateway to provide serverless SQL on Data Warehouses and Lakehouses. Kyuubi builds distributed SQL query engines on top of various kinds of modern modern computing frameworks, e.g., Apache Spark [https://spark.apache.org/], Flink [https://flink.apache.org/], Doris [https://doris.apache.org/], Hive [https://hive.apache.org/], and Trino [https://trino to build a modern data stack. For example, you can use Kyuubi, Spark and Iceberg [https://iceberg.apache.org/] to build and manage Data Lakehouse with pure SQL for both data processing, e.g. ETL, and online0 码力 | 405 页 | 5.26 MB | 1 年前3
Apache Kyuubi 1.4.1 Documentationmulti-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for both data processing e.g. ETL why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the-thrift-jdbcodbc-server] already exists0 码力 | 233 页 | 4.62 MB | 1 年前3
Apache Kyuubi 1.5.1 Documentationmulti-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for both data processing e.g. ETL is why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the- thrift-jdbcodbc-server] already exists0 码力 | 267 页 | 5.80 MB | 1 年前3
Apache Kyuubi 1.6.1 Documentationmulti-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for both data processing e.g. ETL why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the-thrift-jdbcodbc-server] already exists0 码力 | 401 页 | 5.42 MB | 1 年前3
Apache Kyuubi 1.3.0 Documentationmulti-tenant JDBC interface for large-scale data processing and analytics, built on top of Apache Spark™ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies shown each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for both data processing e.g. ETL why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the-thrift-jdbcodbc-server] already exists0 码力 | 199 页 | 4.42 MB | 1 年前3
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