Copyright 2018 ABSA Group Limited
Licensed under the Apache License, Version 2.0 (the "License");
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http://www.apache.org/licenses/LICENSE-2.0
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Enceladus is a Dynamic Conformance Engine which allows data from different formats to be standardized to parquet and conformed to group-accepted common reference (e.g. data for country designation which are DE in one source system and Deutschland in another, can be conformed to Germany).
The project consists of four main components:
The REST API exposes the Enceladus endpoints for creating, reading, updating and deleting the models, as well as other functionalities. The main models used are:
- Runs: Although not able to be defined by users, Runs provide important overview of Standardization & Conformance jobs that have been carried out.
- Schemas: Specifies the schema towards which the dataset will be standardized
- Datasets: Specifies where the dataset will be read from on HDFS (RAW), the conformance rules that will be applied to it, and where it will land on HDFS once it is conformed (PUBLISH)
- Mapping Tables: Specifies where tables with master reference data can be found (parquet on HDFS), which are used when applying Mapping conformance rules (e.g. the dataset uses
Germany, which maps to the master referenceDEin the mapping table) - Dataset Property Definitions: Datasets may be accompanied by properties, but these are not free-form - they are bound by system-wide property definitions.
The REST API exposes a Swagger Documentation UI which documents HTTP exposed endpoints.
It can be found at REST_API_HOST/swagger-ui.html
In order to switch between latest and all (latest + legacy) endpoints, use the UI definition selector (right up corner in Swagger).
This is the user-facing web client, used to specify the standardization schema, and define the steps required to conform a dataset.
The Menas web client calls and is based on the REST API to get the needed entities.
This is a Spark job which reads an input dataset in any of the supported formats and produces a parquet dataset with the Menas-specified schema as output.
This is a Spark job which applies the Menas-specified conformance rules to the standardized dataset.
This is a Spark job which executes both Standardization and Conformance together in the same job
- Maven 3.5.4+
- Java 8
Each module provides configuration file templates with reasonable default values.
Make a copy of the *.properties.template and *.conf.template files in each module's src/resources directory removing the .template extension.
Ensure the properties there fit your environment.
- Without tests:
mvn clean package -Dskip.unit.tests - With unit tests:
mvn clean package - With integration tests:
mvn clean package -Pintegration
- Test coverage:
mvn clean verify -Pcode-coverage
The coverage reports are written in each module's target directory.
- Tomcat 8.5/9.0 installation
- MongoDB 4.0 installation
HADOOP_CONF_DIRenvironment variable, pointing to the location of your hadoop configuration (pointing to a hadoop installation)
Simply copy the rest-api.war file produced when building the project into Tomcat's webapps directory.
Another possible method is building the Docker image based on the existing Dockerfile and deploying it as a container.
There are several ways of deploying Menas:
- Tomcat deployment: copy the
menas.warfile produced when building the project into Tomcat's webapps directory. The"apiUrl"value inpackage.jsonshould be set either before building or after building the artifact and modifying it in place - Docker deployment: build the Docker image based on the existing Dockerfile and deploy it as a container. The
API_URLenvironment variable should be provided when running the container - CDN deployment: copy the built contents in the
distdirectory into your preferred CDN server. The"apiUrl"value inpackage.jsonin thedistdirectory should be set
- Enable the HTTP compression
- Configure
spring.resources.cache.cachecontrol.max-ageinapplication.propertiesof REST API for caching of static resources
- Spark 2.4.4 (Scala 2.11) installation
- Hadoop 2.7 installation
- REST API running instance
- REST API Credentials File in your home directory or on HDFS (a configuration file for authenticating the Spark jobs with Menas)
- Use with in-memory authentication
e.g.
~/rest-api-credential.properties:
- Use with in-memory authentication
e.g.
username=user
password=changeme
- REST API Keytab File in your home directory or on HDFS
- Use with kerberos authentication, see link for details on creating keytab files
- Directory structure for the RAW dataset should follow the convention of
<path_to_dataset_in_menas>/<year>/<month>/<day>/v<dataset_version>. This date is specified with the--report-dateoption when running the Standardization and Conformance jobs. - _INFO file must be present along with the RAW data on HDFS as per the above directory structure. This is a file tracking control measures via Atum, an example can be found here.
<spark home>/spark-submit \
--num-executors <num> \
--executor-memory <num>G \
--master yarn \
--deploy-mode <client/cluster> \
--driver-cores <num> \
--driver-memory <num>G \
--conf "spark.driver.extraJavaOptions=-Denceladus.rest.uri=<rest_api_uri:port> -Dstandardized.hdfs.path=<path_for_standardized_output>-{0}-{1}-{2}-{3} -Dhdp.version=<hadoop_version>" \
--class za.co.absa.enceladus.standardization.StandardizationJob \
<spark-jobs_<build_version>.jar> \
--rest-api-auth-keytab <path_to_keytab_file> \
--dataset-name <dataset_name> \
--dataset-version <dataset_version> \
--report-date <date> \
--report-version <data_run_version> \
--raw-format <data_format> \
--row-tag <tag>
- Here
row-tagis a specific option forraw-formatof typeXML. For more options for different types please see our WIKI. - In case REST API is configured for in-memory authentication (e.g. in dev environments), replace
--rest-api-auth-keytabwith--rest-api-credentials-file
<spark home>/spark-submit \
--num-executors <num> \
--executor-memory <num>G \
--master yarn \
--deploy-mode <client/cluster> \
--driver-cores <num> \
--driver-memory <num>G \
--conf 'spark.ui.port=29000' \
--conf "spark.driver.extraJavaOptions=-Denceladus.rest.uri=<rest_api_uri:port> -Dstandardized.hdfs.path=<path_of_standardized_input>-{0}-{1}-{2}-{3} -Dconformance.mappingtable.pattern=reportDate={0}-{1}-{2} -Dhdp.version=<hadoop_version>" \
--packages za.co.absa:enceladus-parent:<version>,za.co.absa:enceladus-conformance:<version> \
--class za.co.absa.enceladus.conformance.DynamicConformanceJob \
<spark-jobs_<build_version>.jar> \
--rest-api-auth-keytab <path_to_keytab_file> \
--dataset-name <dataset_name> \
--dataset-version <dataset_version> \
--report-date <date> \
--report-version <data_run_version>
<spark home>/spark-submit \
--num-executors <num> \
--executor-memory <num>G \
--master yarn \
--deploy-mode <client/cluster> \
--driver-cores <num> \
--driver-memory <num>G \
--conf "spark.driver.extraJavaOptions=-Denceladus.rest.uri=<rest_api_uri:port> -Dstandardized.hdfs.path=<path_for_standardized_output>-{0}-{1}-{2}-{3} -Dhdp.version=<hadoop_version>" \
--class za.co.absa.enceladus.standardization_conformance.StandardizationAndConformanceJob \
<spark-jobs_<build_version>.jar> \
--rest-api-auth-keytab <path_to_keytab_file> \
--dataset-name <dataset_name> \
--dataset-version <dataset_version> \
--report-date <date> \
--report-version <data_run_version> \
--raw-format <data_format> \
--row-tag <tag>
- In case REST API is configured for in-memory authentication (e.g. in dev environments), replace
--rest-api-auth-keytabwith--rest-api-credentials-file
The Scripts in scripts folder can be used to simplify command lines for running Standardization and Conformance jobs.
Steps to configure the scripts are as follows (Linux):
- Copy all the scripts in
scripts/bashdirectory to a location in your environment. - Copy
enceladus_env.template.shtoenceladus_env.sh. - Change
enceladus_env.shaccording to your environment settings. - Use
run_standardization.shandrun_conformance.shscripts instead of directly invokingspark-submitto run your jobs.
Similar scripts exist for Windows in directory scripts/cmd.
The syntax for running Standardization and Conformance is similar to running them using spark-submit. The only difference is that
you don't have to provide environment-specific settings. Several resource options, like driver memory and driver cores also have
default values and can be omitted. The number of executors is still a mandatory parameter.
The basic command to run Standardization becomes:
<path to scripts>/run_standardization.sh \
--num-executors <num> \
--deploy-mode <client/cluster> \
--rest-api-auth-keytab <path_to_keytab_file> \
--dataset-name <dataset_name> \
--dataset-version <dataset_version> \
--report-date <date> \
--report-version <data_run_version> \
--raw-format <data_format> \
--row-tag <tag>
The basic command to run Conformance becomes:
<path to scripts>/run_conformance.sh \
--num-executors <num> \
--deploy-mode <client/cluster> \
--rest-api-auth-keytab <path_to_keytab_file> \
--dataset-name <dataset_name> \
--dataset-version <dataset_version> \
--report-date <date> \
--report-version <data_run_version>
The basic command to run Standardization and Conformance combined becomes:
<path to scripts>/run_standardization_conformance.sh \
--num-executors <num> \
--deploy-mode <client/cluster> \
--rest-api-auth-keytab <path_to_keytab_file> \
--dataset-name <dataset_name> \
--dataset-version <dataset_version> \
--report-date <date> \
--report-version <data_run_version> \
--raw-format <data_format> \
--row-tag <tag>
Similarly for Windows:
<path to scripts>/run_standardization.cmd ^
--num-executors <num> ^
--deploy-mode <client/cluster> ^
--rest-api-auth-keytab <path_to_keytab_file> ^
--dataset-name <dataset_name> ^
--dataset-version <dataset_version> ^
--report-date <date> ^
--report-version <data_run_version> ^
--raw-format <data_format> ^
--row-tag <tag>
Etc...
The list of options for configuring Spark deployment mode in Yarn and resource specification:
| Option | Description |
|---|---|
| --deploy-mode cluster/client | Specifies a Spark Application deployment mode when Spark runs on Yarn. Can be either client or cluster. |
| --num-executors n | Specifies the number of executors to use. |
| --executor-memory mem | Specifies an amount of memory to request for each executor. See memory specification syntax in Spark. Examples: 4g, 8g. |
| --executor-cores mem | Specifies a number of cores to request for each executor (default=1). |
| --driver-cores n | Specifies a number of CPU cores to allocate for the driver process. |
| --driver-memory mem | Specifies an amount of memory to request for the driver process. See memory specification syntax in Spark. Examples: 4g, 8g. |
| --persist-storage-level level | Advanced Specifies the storage level to use for persisting intermediate results. Can be one of NONE, DISK_ONLY, MEMORY_ONLY, MEMORY_ONLY_SER, MEMORY_AND_DISK (default), MEMORY_AND_DISK_SER, etc. See more here. |
| --conf-spark-executor-memoryOverhead mem | Advanced. The amount of off-heap memory to be allocated per executor, in MiB unless otherwise specified. Sets spark.executor.memoryOverhead Spark configuration parameter. See the detailed description here. See memory specification syntax in Spark. Examples: 4g, 8g. |
| --conf-spark-memory-fraction value | Advanced. Fraction of (heap space - 300MB) used for execution and storage (default=0.6). Sets spark.memory.fraction Spark configuration parameter. See the detailed description here. |
For more information on these options see the official documentation on running Spark on Yarn: https://spark.apache.org/docs/latest/running-on-yarn.html
The list of all options for running Standardization, Conformance and the combined Standardization And Conformance jobs:
| Option | Description |
|---|---|
| --rest-api-auth-keytab filename | A keytab file used for Kerberized authentication to REST API. Cannot be used together with --rest-api-credentials-file. |
| --rest-api-credentials-file filename | A credentials file containing a login and a password used to authenticate to REST API. Cannot be used together with --rest-api-auth-keytab. |
| --dataset-name name | A dataset name to be standardized or conformed. |
| --dataset-version version | A version of a dataset to be standardized or conformed. |
| --report-date YYYY-mm-dd | A date specifying a day for which a raw data is landed. |
| --report-version version | A version of the data for a particular day. |
| --std-hdfs-path path | A path pattern where to put standardized data. The following tokens are expending in the pattern: {0} - dataset name, {1} - dataset version, {2}- report date, {3}- report version. |
The list of additional options available for running Standardization:
| Option | Description |
|---|---|
| --raw-format format | A format for input data. Can be one of parquet, json, csv, xml, cobol, fixed-width. |
| --charset charset | Specifies a charset to use for csv, json or xml. Default is UTF-8. |
| --cobol-encoding encoding | Specifies the encoding of a mainframe file (ascii or ebcdic). Code page can be specified using --charset option. |
| --cobol-is-text true/false | Specifies if the mainframe file is ASCII text file |
| --cobol-trimming-policy policy | Specifies the way leading and trailing spaces should be handled. Can be none (do not trim spaces), left, right, both(default). |
| --copybook string | Path to a copybook for COBOL data format |
| --csv-escape character | Specifies a character to be used for escaping other characters. By default '\' (backslash) is used. * |
| --csv-quote character | Specifies a character to be used as a quote for creating fields that might contain delimiter character. By default " is used. * |
| --debug-set-raw-path path | Override the path of the raw data (used for testing purposes). |
| --delimiter character | Specifies a delimiter character to use for CSV format. By default , is used. * |
| --empty-values-as-nulls true/false | If true treats empty values as nulls |
| --folder-prefix prefix | Adds a folder prefix before the date tokens. |
| --header true/false | Indicates if in the input CSV data has headers as the first row of each file. |
| --is-xcom true/false | If true a mainframe input file is expected to have XCOM RDW headers. |
| --null-value string | Defines how null values are represented in a csv and fixed-width file formats |
| --row-tag tag | A row tag if the input format is xml. |
| --strict-schema-check true/false | If true processing ends the moment a row not adhering to the schema is encountered, false (default) proceeds over it with an entry in errCol |
| --trimValues true/false | Indicates if string fields of fixed with text data should be trimmed. |
Most of these options are format specific. For details see the documentation.
* Can also be specified as a unicode value in the following ways: U+00A1, u00a1 or just the code 00A1. In case empty string option needs to be applied, the keyword none can be used.
The list of additional options available for running Conformance:
| Option | Description |
|---|---|
| --mapping-table-pattern pattern | A pattern to look for mapping table for the specified date. The list of possible substitutions: {0} - year, {1} - month, {2} - day of month. By default the pattern is reportDate={0}-{1}-{2}. Special symbols in the pattern need to be escaped. For example, an empty pattern can be be specified as \'\' (single quotes are escaped using a backslash character). |
| --experimental-mapping-rule true/false | If true, the experimental optimized mapping rule implementation is used. The default value is build-specific and is set in 'application.properties'. |
| --catalyst-workaround true/false | Turns on (true) or off (false) workaround for Catalyst optimizer issue. It is true by default. Turn this off only is you encounter timing freeze issues when running Conformance. |
| --autoclean-std-folder true/false | If true, the standardized folder will be cleaned automatically after successful execution of a Conformance job. |
All the additional options valid for both Standardization and Conformance can also be specified when running the combined StandardizationAndConformance job
./mvn clean verify -Pcode-coverageIf module contains measurable data the code coverage report will be generated on path:
{local-path}\enceladus\{module}\target\jacoco
Standardization and Conformance support plugins that allow executing additional actions at certain times of the computation. To learn how plugins work, when and how their logic is executed, please refer to the documentation.
The purpose of this module is to provide some plugins of additional but relatively elementary functionality. And also to serve as an example how plugins are written: detailed description
A module containing examples of the project usage.
Please see our Contribution Guidelines.
- For Menas migration, there is a useful script available in
scripts/migration/migrate_menas.py
(
dependencies.txtprovided, to install missing ones, runpip install -r scripts/migration/requirements.txt)