From 1215208c56629960318165e53dcec9d9ac3d27a0 Mon Sep 17 00:00:00 2001 From: Eduardo Gonzalez Date: Mon, 24 Jan 2022 08:35:58 +0900 Subject: [PATCH] Add TPC-DS --- tests/tpcdsbench/.gitignore | 11 + tests/tpcdsbench/ACKNOWLEDGEMENTS.md | 5 + tests/tpcdsbench/CONTRIBUTING.md | 59 + tests/tpcdsbench/LICENSE | 201 +++ tests/tpcdsbench/MAINTAINERS.md | 74 + tests/tpcdsbench/README.md | 398 ++++++ .../doc/source/images/architecture.png | Bin 0 -> 84926 bytes .../doc/source/images/create-notebook.png | Bin 0 -> 49344 bytes .../doc/source/images/create-project.png | Bin 0 -> 49825 bytes .../notebooks/run-tpcds-on-spark.ipynb | 1220 +++++++++++++++++ tests/tpcdsbench/src/ddl/create_database.sql | 2 + tests/tpcdsbench/src/ddl/create_tables.sql | 774 +++++++++++ .../src/ddl/individual/call_center.sql | 44 + .../src/ddl/individual/catalog_page.sql | 22 + .../src/ddl/individual/catalog_returns.sql | 40 + .../src/ddl/individual/catalog_sales.sql | 47 + .../src/ddl/individual/customer.sql | 31 + 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create mode 100644 tests/tpcdsbench/.gitignore create mode 100644 tests/tpcdsbench/ACKNOWLEDGEMENTS.md create mode 100644 tests/tpcdsbench/CONTRIBUTING.md create mode 100644 tests/tpcdsbench/LICENSE create mode 100644 tests/tpcdsbench/MAINTAINERS.md create mode 100644 tests/tpcdsbench/README.md create mode 100644 tests/tpcdsbench/doc/source/images/architecture.png create mode 100644 tests/tpcdsbench/doc/source/images/create-notebook.png create mode 100644 tests/tpcdsbench/doc/source/images/create-project.png create mode 100644 tests/tpcdsbench/notebooks/run-tpcds-on-spark.ipynb create mode 100644 tests/tpcdsbench/src/ddl/create_database.sql create mode 100644 tests/tpcdsbench/src/ddl/create_tables.sql create mode 100644 tests/tpcdsbench/src/ddl/individual/call_center.sql create mode 100644 tests/tpcdsbench/src/ddl/individual/catalog_page.sql create mode 100644 tests/tpcdsbench/src/ddl/individual/catalog_returns.sql create mode 100644 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b/tests/tpcdsbench/ACKNOWLEDGEMENTS.md new file mode 100644 index 000000000..e3cd47ab0 --- /dev/null +++ b/tests/tpcdsbench/ACKNOWLEDGEMENTS.md @@ -0,0 +1,5 @@ +# Acknowledgements + +- Credit goes to [*Dilip Biswal*](https://github.com/dilipbiswal), [*Sunitha Kambhampati*](https://github.com/skambha) and [*Xin Wu*](https://github.com/xwu0226) for their implementation. +- Credit goes to [*Suresh Thalamati*](https://github.com/sureshthalamati) and [*Kevin Yu*](https://github.com/kevinyu98) for testing, reviewing and providing valuable feedback. + diff --git a/tests/tpcdsbench/CONTRIBUTING.md b/tests/tpcdsbench/CONTRIBUTING.md new file mode 100644 index 000000000..392662acb --- /dev/null +++ b/tests/tpcdsbench/CONTRIBUTING.md @@ -0,0 +1,59 @@ +## Contributing In General + +Our project welcomes external contributions! If you have an itch, please +feel free to scratch it. + +To contribute code or documentation, please submit a pull request to the [GitHub +repository](https://github.com/IBM/spark-tpc-ds-performance-test). + +A good way to familiarize yourself with the codebase and contribution process is +to look for and tackle low-hanging fruit in the [issue +tracker](https://github.com/IBM/spark-tpc-ds-performance-test/issues). Before embarking on +a more ambitious contribution, please quickly [get in touch](#communication) +with us. + +**We appreciate your effort, and want to avoid a situation where a contribution +requires extensive rework (by you or by us), sits in the queue for a long time, +or cannot be accepted at all!** + +### Proposing new features + +If you would like to implement a new feature, please [raise an +issue](https://github.com/IBM/spark-tpc-ds-performance-test/issues) before sending a pull +request so the feature can be discussed. This is to avoid you spending your +valuable time working on a feature that the project developers are not willing +to accept into the code base. + +### Fixing bugs + +If you would like to fix a bug, please [raise an +issue](https://github.com/IBM/spark-tpc-ds-performance-test/issues) before sending a pull +request so it can be discussed. If the fix is trivial or non controversial then +this is not usually necessary. + +### Merge approval + +The project maintainers use LGTM (Looks Good To Me) in comments on the code +review to indicate acceptance. A change requires LGTMs from two of the +maintainers of each component affected. + +For more details, see the [MAINTAINERS](MAINTAINERS.md) page. + +## Communication + +Please feel free to connect with us: [here](https://github.com/IBM/spark-tpc-ds-performance-test/issues) + +## Setup + +Please add any special setup instructions for your project to help the +developer become productive quickly. + +## Testing + +Please provide information that helps the developer test any changes they +make before submitting. + +## Coding style guidelines + +Beautiful code rocks! Please share any specific style guidelines you might +have for your project. diff --git a/tests/tpcdsbench/LICENSE b/tests/tpcdsbench/LICENSE new file mode 100644 index 000000000..8dada3eda --- /dev/null +++ b/tests/tpcdsbench/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright {yyyy} {name of copyright owner} + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/tests/tpcdsbench/MAINTAINERS.md b/tests/tpcdsbench/MAINTAINERS.md new file mode 100644 index 000000000..3bfcdc296 --- /dev/null +++ b/tests/tpcdsbench/MAINTAINERS.md @@ -0,0 +1,74 @@ +## Maintainers Guide + +This guide is intended for maintainers — anybody with commit access to one or +more Developer Journey repositories. + +## Methodology: + +A master branch. This branch MUST be releasable at all times. Commits and +merges against this branch MUST contain only bugfixes and/or security fixes. +Maintenance releases are tagged against master. + +A develop branch. This branch contains your proposed changes. + +The remainder of this document details how to merge pull requests to the +repositories. + +## Merge approval + +The project maintainers use LGTM (Looks Good To Me) in comments on the code +review to indicate acceptance. A change requires LGTMs from two of the members +of the [cda-journey-dev-admins](https://github.com/orgs/IBM/teams/cda-journey-dev-admins) +team. If the code is written by a member, the change only requires one more +LGTM. + +## Reviewing Pull Requests + +We recommend reviewing pull requests directly within GitHub. This allows a +public commentary on changes, providing transparency for all users. When +providing feedback be civil, courteous, and kind. Disagreement is fine, so +long as the discourse is carried out politely. If we see a record of uncivil +or abusive comments, we will revoke your commit privileges and invite you to +leave the project. + +During your review, consider the following points: + +### Does the change have impact? + +While fixing typos is nice as it adds to the overall quality of the project, +merging a typo fix at a time can be a waste of effort. +(Merging many typo fixes because somebody reviewed the entire component, +however, is useful!) Other examples to be wary of: + +Changes in variable names. Ask whether or not the change will make +understanding the code easier, or if it could simply a personal preference +on the part of the author. + +Essentially: feel free to close issues that do not have impact. + +### Do the changes make sense? + +If you do not understand what the changes are or what they accomplish, +ask the author for clarification. Ask the author to add comments and/or +clarify test case names to make the intentions clear. + +At times, such clarification will reveal that the author may not be using +the code correctly, or is unaware of features that accommodate their needs. +If you feel this is the case, work up a code sample that would address the +issue for them, and feel free to close the issue once they confirm. + +### Is this a new feature? If so: + +Does the issue contain narrative indicating the need for the feature? If not, +ask them to provide that information. Since the issue will be linked in the +changelog, this will often be a user's first introduction to it. + +Are new unit tests in place that test all new behaviors introduced? If not, do +not merge the feature until they are! +Is documentation in place for the new feature? (See the documentation +guidelines). If not do not merge the feature until it is! +Is the feature necessary for general use cases? Try and keep the scope of any +given component narrow. If a proposed feature does not fit that scope, +recommend to the user that they maintain the feature on their own, and close +the request. You may also recommend that they see if the feature gains traction +amongst other users, and suggest they re-submit when they can show such support. diff --git a/tests/tpcdsbench/README.md b/tests/tpcdsbench/README.md new file mode 100644 index 000000000..383f775d6 --- /dev/null +++ b/tests/tpcdsbench/README.md @@ -0,0 +1,398 @@ +# Explore Spark SQL and its performance using TPC-DS workload + +> Data Science Experience is now Watson Studio. Although some images in this code pattern may show the service as Data Science Experience, the steps and processes will still work. + +[Apache Spark](https://spark.apache.org) is a popular distributed data processing engine that is built around speed, ease of use and sophisticated analytics, with APIs in Java, Scala, Python, R, and SQL. Like other data processing engines, Spark has a unified optimization engine that computes the optimal way to execute a workload with the main purpose of reducing the disk IO and CPU usage. + +We can evaluate and measure the performance of Spark SQL using the TPC-DS benchmark. [TPC-DS](http://www.tpc.org/tpcds) is a widely used industry standard decision support benchmark that is used to evaluate performance of data processing engines. Given that TPC-DS exercises some key data warehouse features, running TPC-DS successfully reflects the readiness of Spark in terms of addressing the need of a data warehouse application. Apache Spark v2.0 supports all the ninety-nine decision support queries that is part of this TPC-DS benchmark. + +This Code Pattern is aimed at helping Spark developers quickly setup and run the TPC-DS benchmark in their own development setup. + +When the reader has completed this Code Pattern, they will understand the following: + +* How to setup the TPC-DS toolkit +* How to generate TPC-DS datasets at different scale factor +* How to create Spark database artifacts +* How to run TPC-DS benchmark queries on Spark in local mode and see the results +* Things to consider when increasing the data scale and run against a spark cluster + +![Architecture diagram](doc/source/images/architecture.png) + +## Flow +* Commandline + 1. Create the spark tables with pre-generated dataset. + 2. Run the entire query set or a subset of queries and monitor the results. +* Notebook + 1. Create the spark tables with pre-generated dataset. + 2. Run the entire query set or individual query. + 3. View the query results or performance summary. + 4. View the performance graph. + +## Included components + +* [Apache Spark](https://spark.apache.org/): An open-source, fast and general-purpose cluster computing system +* [Jupyter Notebook](https://jupyter.org/): An open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. + +## Featured technologies + +* [Data Science](https://medium.com/ibm-data-science-experience/): Systems and scientific methods to analyze +structured and unstructured data in order to extract knowledge and insights. +* [Artificial Intelligence](https://medium.com/ibm-data-science-experience): Artificial intelligence can be +applied to disparate solution spaces to deliver disruptive technologies. +* [Python](https://www.python.org/): Python is a programming language that lets you work more quickly +and integrate your systems more effectively. + +# Steps + +There are two modes of exercising this Code Pattern: +* Run locally using a simple interactive command line shell script. +* [Run using a Jupyter notebook in Watson Studio](#run-using-a-jupyter-notebook-in-watson-studio). + +## Run locally +1. [Clone the repository](#1-clone-the-repository) +2. [Setup development tools (Optional)](#2-setup-development-tools-optional) +3. [Install Spark](#3-install-spark) +4. [Run the script](#4-run-the-script) + +### 1. Clone the repository + +Clone the `spark-tpc-ds-performance-test` repo locally. In a terminal, run: + +``` +$ git clone https://github.com/IBM/spark-tpc-ds-performance-test +``` + +### 2. Setup development tools (Optional) + +Due to licensing restrictions, the TPCDS toolkit is not included as part of the code pattern. Instead, a pre-generated data set with 1GB scale factor is +included in this pattern. If you want to work with a data set with larger scale factor or explore learning the full life sycle of setting up TPCDS, you can + download the tool kit from [TPC-DS](http://www.tpc.org/tpcds) and compile in your development environment. + +Make sure the required development tools are installed in your platform. This Code Pattern is supported on Mac and Linux platforms only. Depending on your platform, run the following command to install the necessary development tools: + +* **Ubuntu:** +``` $ sudo apt-get install gcc make flex bison byacc git ``` +* **CentOS/RHEL:** +``` $ sudo yum install gcc make flex bison byacc git ``` +* **MacOS:** +``` $ xcode-select --install ``` + +To compile the toolkit you need to the following : + +``` +unzip +cd /tools +make clean +make OS= +``` + +### 3. Install Spark + +To successfully run the TPC-DS tests, Spark must be installed and pre-configured to work with an [Apache Hive](https://hive.apache.org/) metastore. + +Perform 1 or more of the following options to ensure that Spark is installed and configured correctly. Once completed, modify ```bin/tpcdsenv.sh``` to set `SPARK_HOME` pointing to your Spark installation directory. + +**Option 1** - If you already have Spark installed, complete the following steps to ensure your Spark version is properly configured: + +``` +$ cd $SPARK_HOME +$ bin/spark-shell + + // Enter the following command at the scala prompt + scala> spark.conf + scale> spark.conf.get("spark.sql.catalogImplementation") + res5: String = hive + scala> +``` +*Note:* You must exit out of the spark-shell process or you will encounters errors when performing the TPC-DS tests. + +If the prompt returns `String = hive`, then your installation is properly configured. + +**Option 2** - If you don't have an installed Spark version, or your current installation is not properly configured, we suggest trying to pull down version 2.2.0 from the Spark [downloads page](https://spark.apache.org/downloads.html). This version should be configured to work with Apache Hive, but please run the test in the previous option to make sure. + +**Option 3** - The last option available is it to download and build it yourself. The first step is to clone the Spark repo: +``` +$ git clone https://github.com/apache/spark.git +``` +Then build it using these [instructions](https://spark.apache.org/docs/latest/building-spark.html). Please make sure to build Spark with Hive support by following the `Building With Hive and JDBC Support` section. + +### 4. Run the script + +*Note:* Verify that the `bin/tpcdsenv.sh` script has `SPARK_HOME` setup correctly. + +Now that we have Spark setup and the TPC-DS scripts downloaded, we are ready to setup and start running the TPC-DS queries using the `bin/tpcdsspark.sh` utility script. This driver script will allow you to compile the TPC-DS toolkit to produce the data and the queries, and then run them to collect results. + +Perform the following steps to complete the execution of the script: + +``` + $ cd spark-tpc-ds-performance-test + $ bin/tpcdsspark.sh + +============================================== +TPC-DS On Spark Menu +---------------------------------------------- +SETUP + (1) Create spark tables +RUN + (2) Run a subset of TPC-DS queries + (3) Run All (99) TPC-DS Queries +CLEANUP + (4) Cleanup + (Q) Quit +---------------------------------------------- +Please enter your choice followed by [ENTER]: +``` + +#### Setup Option: "(1) - Create Spark Tables" + +This option creates the tables in the database name specified by `TPCDS_DBNAME` defined in `bin/tpcdsenv.sh`. The default name is `TPCDS` but can be changed if needed. The created tables are based on the pre-generated data. + +The SQL statements to create the tables can be found in `src/ddl/individual`, and are created in parquet format for efficient processing. + +> Due to licensing restrictions, the TPCDS toolkit is not included as part of the code pattern. Instead, a pre-generated data set with 1GB scale factor is + included in this pattern. If you want to work with a data set with larger scale factor or explore learning the full life sycle of setting up TPCDS, you can + download the tool kit from [TPC-DS](http://www.tpc.org/tpcds) and compile in your development environment. Here are the instructions that describes how + to compile the tool kit and generate data. + + +1. Compile the toolkit + + ``` + unzip + cd /tools + make clean + make OS= + # (platform can be 'macos' or 'linux'). + ``` + +2. Generate the data. + + ``` + cd /src/toolkit/tools + ./dsdgen -dir -scale -verbose y -terminate n + # data_gen_dir => The output directory where data will be generated at. + # scale_factor => The scale factor of data. + +3. Generate the queries. + + The `dsqgen` utility in the tpcds toolkit may be used to generate the queries. Appropiate options should be passed to this utility. A typical example of its usage is: + + ``` + cd /tools + ./dsqgen -VERBOSE Y -DIALECT -DIRECTORY -SCALE -OUTPUT_DIR + ``` + +Below is example output for when this option is chosen. + +``` +============================================== +TPC-DS On Spark Menu +---------------------------------------------- +SETUP + (1) Create spark tables +RUN + (2) Run a subset of TPC-DS queries + (3) Run All (99) TPC-DS Queries +CLEANUP + (4) Cleanup + (Q) Quit +---------------------------------------------- +Please enter your choice followed by [ENTER]: 1 +---------------------------------------------- + +INFO: Creating tables. Will take a few minutes ... +INFO: Progress : [########################################] 100% +INFO: Spark tables created successfully.. +Press any key to continue +``` + +#### Run Option: "(2) - Run a subset of TPC-DS queries" + +A comma separated list of queries can be specified in this option. The result of each query in the supplied list is generated in `TPCDS_WORK_DIR`, with a default directory location of `work`. The format of the result file is `query.res`. + +A summary file named `run_summary.txt` is also generated. It contains information about query number, execution time and number of rows returned. + +*Note:* The query number is a two digit number, so for query 1 the results will be in `query01.res`. + +*Note:* If you are debugging and running queries using this option, make sure to save `run_summary.txt` after each of your runs. + +``` +============================================== +TPC-DS On Spark Menu +---------------------------------------------- +SETUP + (1) Create spark tables +RUN + (2) Run a subset of TPC-DS queries + (3) Run All (99) TPC-DS Queries +CLEANUP + (4) Cleanup toolkit + (Q) Quit +---------------------------------------------- +Please enter your choice followed by [ENTER]: 2 +---------------------------------------------- + +Enter a comma separated list of queries to run (ex: 1, 2), followed by [ENTER]: +1,2 +INFO: Checking pre-reqs for running TPC-DS queries. May take a few seconds.. +INFO: Checking pre-reqs for running TPC-DS queries is successful. +INFO: Running TPCDS queries. Will take a few minutes depending upon the number of queries specified.. +INFO: Progress : [########################################] 100% +INFO: TPCDS queries ran successfully. Below are the result details +INFO: Individual result files: spark-tpc-ds-performance-test/work/query.res +INFO: Summary file: spark-tpc-ds-performance-test/work/run_summary.txt +Press any key to continue +``` + +#### Run Option: "(3) - Run all (99) TPC-DS queries" + +The only difference between this and option `(5)` is that all 99 TPC-DS queries are run instead of a subset. + +*Note:* If you are running this on your laptop, it can take a few hours to run all 99 TPC-DS queries. + +``` +============================================== +TPC-DS On Spark Menu +---------------------------------------------- +SETUP + (1) Create spark tables +RUN + (2) Run a subset of TPC-DS queries + (3) Run All (99) TPC-DS Queries +CLEANUP + (4) Cleanup toolkit + (Q) Quit +---------------------------------------------- +Please enter your choice followed by [ENTER]: 3 +---------------------------------------------- +INFO: Checking pre-reqs for running TPC-DS queries. May take a few seconds.. +INFO: Checking pre-reqs for running TPC-DS queries is successful. +INFO: Running TPCDS queries. Will take a few minutes depending upon the number of queries specified.. +INFO: Progress : [########################################] 100% +INFO: TPCDS queries ran successfully. Below are the result details +INFO: Individual result files: spark-tpc-ds-performance-test/work/query.res +INFO: Summary file: spark-tpc-ds-performance-test/work/run_summary.txt +Press any key to continue +``` + +#### Cleanup option: "(4) - Cleanup" + +This will clean up all of the files generated during option steps 1, 2, and 3. If you use this option, make sure to run the setup steps (1) before running queries using option 2 and 3. + +#### Cleanup option: "(Q) - Quit" + +This will exit the script. + +## Run using a Jupyter notebook in Watson Studio + +1. [Sign up for Watson Studio](#1-sign-up-for-watson-studio) +2. [Create the notebook](#2-create-the-notebook) +3. [Run the notebook](#3-run-the-notebook) +4. [Save and Share](#4-save-and-share) + +### 1. Sign up for Watson Studio + +Sign up for IBM's [Watson Studio](https://dataplatform.cloud.ibm.com/). By creating a project in Watson Studio a free tier ``Object Storage`` service will be created in your IBM Cloud account. + +> Note: When creating your Object Storage service, select the ``Free`` storage type in order to avoid having to pay an upgrade fee. + +Take note of your service names as you will need to select them in the following steps. + +### 2. Create the notebook + +* In [Watson Studio](https://dataplatform.cloud.ibm.com/), click on `Create notebook` to create a notebook. +* Create a project if necessary, provisioning an object storage service if required. +* In the `Assets` tab, select the `Create notebook` option. +* Select the `From URL` tab. +* Enter a name for the notebook. +* Optionally, enter a description for the notebook. +* Enter this Notebook URL: https://github.com/IBM/spark-tpc-ds-performance-test/blob/master/notebooks/run-tpcds-on-spark.ipynb +* Select the free Anaconda runtime. +* Click the `Create` button. + +![](doc/source/images/create-notebook.png) + +### 3. Run the notebook + +When a notebook is executed, what is actually happening is that each code cell in +the notebook is executed, in order, from top to bottom. + +Each code cell is selectable and is preceded by a tag in the left margin. The tag +format is `In [x]:`. Depending on the state of the notebook, the `x` can be: + +* A blank, this indicates that the cell has never been executed. +* A number, this number represents the relative order this code step was executed. +* A `*`, this indicates that the cell is currently executing. + +There are several ways to execute the code cells in your notebook: + +* One cell at a time. + * Select the cell, and then press the `Play` button in the toolbar. +* Batch mode, in sequential order. + * From the `Cell` menu bar, there are several options available. For example, you + can `Run All` cells in your notebook, or you can `Run All Below`, that will + start executing from the first cell under the currently selected cell, and then + continue executing all cells that follow. +* At a scheduled time. + * Press the `Schedule` button located in the top right section of your notebook + panel. Here you can schedule your notebook to be executed once at some future + time, or repeatedly at your specified interval. + +### 4. Save and Share + +#### How to save your work: + +Under the `File` menu, there are several ways to save your notebook: + +* `Save` will simply save the current state of your notebook, without any version + information. +* `Save Version` will save your current state of your notebook with a version tag + that contains a date and time stamp. Up to 10 versions of your notebook can be + saved, each one retrievable by selecting the `Revert To Version` menu item. + +#### How to share your work: + +You can share your notebook by selecting the “Share” button located in the top +right section of your notebook panel. The end result of this action will be a URL +link that will display a “read-only” version of your notebook. You have several +options to specify exactly what you want shared from your notebook: + +* `Only text and output`: will remove all code cells from the notebook view. +* `All content excluding sensitive code cells`: will remove any code cells + that contain a *sensitive* tag. For example, `# @hidden_cell` is used to protect + your dashDB credentials from being shared. +* `All content, including code`: displays the notebook as is. +* A variety of `download as` options are also available in the menu. + +## Considerations while increasing the scale factor. +This Code Pattern walks us through the steps that need to be performed to run the TPC-DS +benchmark with the qualification scale factor(1GB). Since this is a performance benchmark, typically +we need to run the benchmark with varying scale factors to gauge the throughput of the underlying data +processing engine. In the section below, we will briefly touch on things to be considered while increasing +the data and running the workload against a production cluster. + +* Generation of the data in larger scale factor: + In order to increase the scale, please follow the section titled "Scaling and Database Population" in + the [benchmark spec](http://www.tpc.org/tpc_documents_current_versions/pdf/tpc-ds_v2.2.0.pdf). +* Movement of data to the distributed file system: + After generating the data, we need to copy or move them to the underlying distributed file system (typically hdfs) + that your spark cluster is configured to work with. +* Creation of spark tables: + Modify the create table ddl script to change the path to the location of the data after the above copy step. + Additionally we may consider to partition the fact tables for better performance. +* We need to tune several spark configs to get optimal performance. Some of them are discussed in the following + links. + * [Troubleshooting and Tuning spark for heavy work loads](https://developer.ibm.com/hadoop/2016/07/18/troubleshooting-and-tuning-spark-for-heavy-workloads) + * [Scaling up to 100TB](https://developer.ibm.com/hadoop/2017/02/07/experiences-comparing-big-sql-and-spark-sql-at-100tb) + +# Learn more + +* **Data Analytics Code Patterns**: Enjoyed this Code Pattern? Check out our other [Data Analytics Code Patterns](https://developer.ibm.com/technologies/data-science/) +* **AI and Data Code Pattern Playlist**: Bookmark our [playlist](https://www.youtube.com/playlist?list=PLzUbsvIyrNfknNewObx5N7uGZ5FKH0Fde) with all of our Code Pattern videos +* **Watson Studio**: Master the art of data science with IBM's [Watson Studio](https://dataplatform.cloud.ibm.com/) +* **Spark on IBM Cloud**: Need a Spark cluster? Create up to 30 Spark executors on IBM Cloud with our [Spark service](https://cloud.ibm.com/catalog/services/apache-spark) + +# License +This code pattern is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the [Developer Certificate of Origin, Version 1.1 (DCO)](https://developercertificate.org/) and the [Apache Software License, Version 2](http://www.apache.org/licenses/LICENSE-2.0.txt). + +[Apache Software License (ASL) FAQ](http://www.apache.org/foundation/license-faq.html#WhatDoesItMEAN) diff --git a/tests/tpcdsbench/doc/source/images/architecture.png b/tests/tpcdsbench/doc/source/images/architecture.png new file mode 100644 index 0000000000000000000000000000000000000000..e84107c9b6597f42381f5ee109103cece8c3b623 GIT binary patch literal 84926 zcmeFZ2UJwswkBMNLr!ZPZYl~?K$|h*H}hL28DVPx^KgP1YWZlUQ$&;q1;ZRP(BY(s5N-Y zXB35UEh9argu2ohu3#P}|7A#LBcVc!$E~(rtSfaRB*G^yTz| z6ue31pr~|_Y+xVFej&2(fVZ-6MU>(N8FiPg$=(_Bo_keNTbsmo>{~<)q;si4dnyNl z9nqTyWjY>{9G+XO+iRR^q2*r?J$$C)>&PEwy|~oHl^mxh0{!j&nc+>0VUzpP>V`>X#qWB2;~*Jb$cHx~6D zRrs5J|D(MB4afdw^8cy4|A%8$PewLbf4R31R%)@esV%tecrJ$aa9{Ji1qJ)gjb&2S zvlmQj>@7xZ2dk_qqg^(Xlqw=VYf5F|Fb0(~HKN{MM^em<-hSpYZ7>gJn$Q;XF8KUL 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