Data Pipelines

Pipeline Configuration

On the Pipeline canvas, user can ingest and blend incoming data (batch/streaming) from any source, process the data, apply ML algorithms and emit in the required emitter/data warehouse/storage system by leveraging drag-and-drop operators.

User can create a data pipeline within a desired project. The Pipeline listing page will display all the batch and streaming data pipeline tiles. User can create a pipeline, download sample project and upload pipeline from options available at the top right of the listing page.

For AWS, the pipelines can be deployed on Databricks and EMR.

The home page of the data pipelines is explained below:

The following image shows the homepage of data pipeline and the next diagram shows actions that can be performed on the pipeline.

Within the Data Pipeline, user can perform the following actions:

• Design, Import and Export real-time data pipeline.

• Deploy pipelines on AWS Cloud platform.

• Deploy pipelines on either Cloud vendor-Databricks or Amazon EMR.

• Drag, drop and connect operators to create applications.

• Create Datasets and reuse them in different pipelines.

• Create Models from the analytics operators.

• Monitor detailed metrics of each task and instance.

• Run PMML-based scripts in real-time on every incoming message.

• Explore real-time data using interactive charts and dashboards.

There could be a business scenario where the pipeline design remains the same for multiple pipelines, yet the configurations differ.

For an e.g. A user has 3 different Kafka topics containing similar data that needs to go through same kind of processing (Kafka --> Filter --> ExpressionEvaluator --> HDFS). Normally, a user would create two clones of the same pipeline and edit the configuration.

The easier way of doing this is now user can create a pipeline template that comprises of the pipeline structure and multiple instances which only differ in configuration and inherit the pipeline structure of the pipeline template.

Download Configuration Template

The user can use the Download Configuration Template option to download a json file that contains all the component configurations used in a data pipeline. This will be used to create an instance.

Create Instance

The user will modify the downloaded json file using ‘Download Configuration Template’ option to apply configuration changes for his first instance. For eg. The user changes the topic name in the Kafka channel and saves the modified json file.

The user can create the first instance by clicking the ellipses of the pipeline tile and click View Instance.

On clicking ‘View Instance’ a page opens where user can create an instance by providing instance name and provide configuration used for pipeline instance creation. Provide a name and paste the content of the modified json file that has the amended Kafka topic name.

Click on ‘CREATE’ button to create instance or optionally ‘CREATE & LAUNCH’ to create and launch the instance.

The user can create a template in the data pipeline listing page. Thus, a template can have multiple pipeline instances running using the same set of components and different configurations.

Note:

-The user must create an instance to convert a pipeline into a pipeline template.

-T stands for Template.

-The Template tile shows following details: Total Instances, Active Instances, Starting Instances, Stopped Instances, Instances in Error.

The user can create further instances and view existing instances as well:

Note:

The user can monitor the instance, view history, view audit activity, update instance, view the instance configuration and also view the instance submission logs by clicking at the options available on the instance tile. (shown below)

The user can delete the instance. The user cannot delete a template if instances exist. To delete a template, all instances should be deleted first.

Start/Stop Template

The user can Start/Stop the template by clicking at the Start/Stop button available at the template. All the instances must be deleted to delete the template.

Once the user selects Start, the user will be required to select the instance(s):

Click Start.

Likewise, to stop the instances, click Stop.

NOTE: When the data pipeline page is loaded, each tile is loaded one by one, in order to prevent the page from giving an error in loading large number of pipelines or heavy pipelines.

Creating a Spark Pipeline

If you are creating a data pipeline for the first time, you need to ensure that you are in a new project. Click Projects Manage Workspaces on Gathr main menu. The user can create a new workspace or enter in an already existing the left navigation panel. You can create a new project or enter in an already existing project. Now go to the Pipeline page from the options listed inside Project. On this Pipeline page, you may create a new pipeline or edit/work in an existing pipeline. To create a new pipeline, click on the +icon at the right of the screen. You are ready to create a pipeline on the grid-canvas.

Make sure that the Local connection is established. The green arrow above the pipeline canvas denotes that the connection is established.

Drag components on the pipeline builder (pipeline grid-canvas) from the right panel and connect the components.

Select and right click on a component to configure its properties (view Data Sources, Processors, Data Science and Emitters sections, to understand the components).

The components that require connection details have an option to select the connection name from the existing list of connection templates.

The user while configuring component properties can check-mark the Override Credential box to override existing connection credentials.

In such scenarios the user will need to provide any other valid credential details through which the component connection can be authenticated.

Note: Make sure to provide valid credentials if Override Credentials option is chosen, as it can result to pipeline execution failure. For any such scenario, the actual reason for failure can be traced by looking at the Pipeline Submission Logs.

Once the configuration is done, click on the floppy icon to save the pipeline.

Upload Jars/Include Executable Python

User has an option to upload Jars or include Executable Python file in the Data Pipeline configuration.

To upload Jars click on the Upload Jars icon and then on the Upload Jars tab do as follows:

Click on the upload icon and select files to be uploaded.

To include Executable Python files do as follows:

User has three options out of which any one can be selected to include the Executable Python file.

Note:

- Save button is provided in the NFS and HDFS pex file upload options whereas in Upload option, once the file is uploaded it is automatically saved.

- Delete option can be used for NFS, HDFS & Upload options to delete any of the existing pex file and then re-upload a new one.

- At a time, a single pex file can be associated with a pipeline, linking a new pex file will override the existing one.

- In case of Pipeline Import/Clone, Pex File Configuration will be visible in case if the pex file is provided either by NFS or HDFS option. User can specify the path for the pex file upload.

- If user wants to add pex file via Upload option, it can be only done by editing the imported or cloned pipeline later.

Enable Impersonation

The user can also enable impersonation from the inspect session arrow window within a workspace.

Enter the details of the pipeline in Pipeline Definition window.

The pipeline definition options are explained below:

When you click on MORE, the below mentioned configuration appears:

If a streaming pipeline fails to successfully run, the user can reschedule and restart the pipeline the user will have to configure the pipeline. Click on the pipeline tile ellipses and select Pipeline configuration/Edit options. The user can also specify the auto restart pipeline option while importing/exporting a pipeline.

Note:

• When you create a pipeline, the associated or used schema is saved as metadata under message configuration.

• Components are batch and streaming based.

• Likewise, in Spark standalone the application cores must be greater than or equal to the executor cores.

Error Handler Configuration

Error handler configuration feature enables you to handle the pipeline errors.

Error log target are of three types: RabbitMQ, Kafka and Logger.

Error Handler helps you to log pipeline errors to logger file or on Kafka or RMQ. You can refer the log file or message queues to view those errors whenever required. All these errors will be visible on graphs under Summary view of pipelines under Application Errors Tab.

Gathr gives you an option to handle errors after your data pipeline is created. When you save a pipeline, specify whether you want to enable error handler or not. Either Kafka or RMQ is selected as an error log target. Whenever an error happens, the pipeline will log the errors to the configured error handler. By default, errors will always be published to log files. If you want to disable logging errors to these queues, disable error handler while saving or updating actions on the pipeline. If error handler is disabled, error graphs will not be visible and errors will not be available on the errors listing page.

Note: If RMQ or Kafka is down, the data pipeline will work. For the visibility of error graph default Elasticsearch connection should be in working state. Also, for DQM processor it is mandatory to keep this flag checked to use the Send to Error Feature. If user disables error handler, error monitoring graphs will not be visible.

For DQM processor it is mandatory to keep this flag checked to use the Send to Error Feature.

Based on error handler target, you will be asked for target specific configurations like connection name, partitions and topic administration in case of Kafka and ttl in case of RabbitMQ.

Once you save or update the pipeline, and start the pipeline, you can view errors in the configured queue or topic as well as in pipeline logs.

Note: By default pipeline errors will be logged in Logger.

For viewing error graphs, go to the Data pipeline tab on Data Pipeline Home page, click on the three dots of the pipeline tile and click on View Summary.

Status Alert Configuration

Upon enabling the Status Alert option, you can send Alert/Message to a Kafka Topic for any change in the pipeline status.

Actions on Pipeline

To view the screen as shown below, go to the home page of Data pipeline and click on the three dots of a pipeline’s widget.

As shown in the snippet below, the following actions can be performed on a pipeline.

Note: In the Pipeline configuration, if a streaming pipeline fails to successfully run, the user can reschedule and restart the pipeline the user will have to configure the pipeline. Click on the pipeline tile ellipses and select Pipeline configuration/Edit options and update the configuration. The user can also specify the auto restart pipeline option while importing/exporting a pipeline.

Upon clicking MORE PROPERTIES, further options appear within the Pipeline Configuration window. These are explained below:

Click Update to save the details.

Pipeline Run History

User can view the details of pipeline by clicking the pipeline tile ellipses option. The user can monitor the batch pipeline activity and keep a track of the run details for each instance such as the total number of records in input and output process and time taken by the pipeline to run completely. After the successful run of the pipeline, click the ellipses of the pipeline tile to view the batch monitoring history.

In the pipeline history page, user will be able to view the batch monitoring table and the run history of the pipeline with that table.

Note: If there are multiple files that the user is reading then the entire list of file names along with locations will be displayed.

Pipeline Submission Logs

The pipeline logs either be viewed by clicking on application id or clicking on Pipeline Submission Logs option available under.

Note: Pipeline Submission Logs will not available on Apache Standalone ENV when pipeline is in cluster mode.

All the above Logs are also displayed under different color schemes.

Error Logs:

These logs are displayed in RED color.

Warning Logs

These logs are displayed in Orange color.

Rest of the logs

Rest of the logs are all displayed in Black color.

Two additional properties are added in Default Section on the Configuration page.

Tail Logs Server Port: Listening port number where tail command will listen incoming streams of logs, default is 9001.

Tail Logs Max Buffer Size: Maximum number of lines that can be stored on browser, default is 1000.

Deploy a Pipeline

Configure Job brings you to deploy the pipeline on cloud clusters.

For an AWS platform you can deploy pipelines on Databricks and EMR.

After a pipeline is created, you have two options, either deploy the pipeline on any of the existing cluster from Databricks or EMR. the second option is to deploy the pipeline of a job cluster. However this depends on the type of job that your will run.

You can choose the if the pipeline will be deployed on the Job Clusters or a Long Running or Interactive Clusters. When you want to deploy a pipeline on a Job cluster, the rule is that the job ends as soon as the pipeline is completed. However for an Interactive and Long running jobs, you can deploy the pipelines on the clusters created for Databricks and EMR.

To understand the Pipeline Clusters, you can also refer the Cluster List View section of the Administration Guide.

NOTE: Only a workspace user who has DB or EMR credentials can deploy a pipeline on either and you can do so by clicking on the Configure Job button on the pipeline tile.

In order to deploy a pipeline, configure the same on Databricks or EMR.

Once you click on Configure Job, you will be redirected to the Job Configuration Page.

Configure Job on Databricks

Under Databricks, following are the properties that you would need to configure.

Databricks has two types of clusters: Interactive and Job.

Configuration of a Databricks cluster is divided in six categories: Cluster, Schedule, Alerts, Runs, Timeout and Retries.

Interactive clusters (Long-running cluster)

Interactive clusters are used to analyze data collaboratively with interactive notebooks.

All the Interactive clusters associated with pre-configured Databricks instance appear here. These clusters will be running even after the jobs running on them are finished; user has to manually terminate these cluster.

Databricks will allow you to deploy your pipeline on an existing Interactive cluster.

Job clusters (New cluster)

Job clusters are associated with a particular pipeline and they will be active as long as the pipeline is running. They will be terminated as soon as the pipeline is stopped.

Configure Job on EMR

Under EMR, following are the properties that you would need to configure.

EMR has two types of clusters: Interactive and Job.

Interactive clusters

Interactive clusters are used to analyze data collaboratively with interactive notebooks.

All the Interactive clusters associated with pre-configured EMR instance appear here. These clusters will be running even after the jobs running on them are finished; user has to manually terminate these Cluster.

EMR will allow you to deploy your pipeline on an existing Interactive Cluster.

Job Clusters (New Cluster)

Job Clusters are associated with a particular pipeline and they will be active as long as the pipeline is running. They will be terminated as soon as the pipeline is stopped.

Job Clusters are associated with a particular pipeline and they will be active as long as the pipeline is running. They will be terminated as soon as the pipeline is stopped.

User can now create tags by giving some key value pairs. Once added, they will get associated with the EMR Cluster which can be seen on the Instance page.It can be added for both, the job cluster and the long running cluster.

Pipeline Management

Gathr provides tools, such as Auto Inspect, Upload Pipeline, Data Preparation, Download sample project, and Monitor, to manage pipelines at all the stages of its lifecycle.

Local Inspect

Local Inspect connection can be created at any time and from anywhere in the workspace. However, if you do not want to create a connection explicitly, the connection would be initialized automatically when you start Auto-Inspection. However, you can disconnect from it after the pipeline has been created. It is not mandatory for Local Inspect to be in initialized state for a pipeline to be edited and saved.

While connecting the Local Inspect session, you can specify additional extra java options by clicking the Spark Parameters option. This parameter is optional.

In Spark Parameter, you can supply Extra Java Options for the inspect JVM process. These parameters are disabled for a superuser.

Validations

This section provides the notifications details while performing any unsupported operations in pipeline and you will be notified regarding this unsupported operation via the window shown below:

Any operation that will cause an Error, Warnings or Recommendations, will be listed under Pipeline Notifications.

Below mentioned are some of the change notifications that will be generated for the below processors:

Aggregation

Multiple aggregation operations on a Streaming Dataset are unsupported.

Distinct

Distinct is not supported to be used with Streaming Dataset.

Sort

Sorting is not supported on streaming Datasets, unless it is with aggregation in Complete output mode.

Dedup

Dedup is not supported after aggregation on a streaming Dataset.

Limit

Limits are not supported with Streaming Datasets.

Union

Union is not supported between Streaming and Batch Datasets.

Join

List of Join Operations in Spark:

Auto Inspection

When your component is inspect ready, slide the Auto Inspect button.

Auto Inspection lets you verify the component’s data with the schema as per the configuration of the component. This happens as soon as the component is saved.

Auto inspect is done manually therefore while editing the pipeline, you can choose to not inspect the pipeline. When you edit a pipeline, auto inspect will be off by default.

While editing, you can add/remove new components in the pipeline without auto inspect.

You can auto inspect the properly configured components of the pipeline if the pipeline is not entirely configured.

Also, the auto inspection can be stopped at any point, even while schema is being detected, and pipeline can be saved even if the inspection hangs or inspect fails.

Clicking on this button stops the auto-inspection of an existing pipeline.

If one of the component is not configured, the auto inspect will run for the configured components only. The auto inspect window will show a notification in Red for the unconfigured component.

A single component can be inspected by clicking on the eye icon on top of the components as shown below:

If a pipeline is saved without inspection, the pipeline tile on the data pipeline homepage will show the warning-icon of not inspected, Once you clilck on the exclamation mark, it shows the Pipeline Notifications.

Once the data is loaded from the source it is automatically played through the pipeline. During auto inspect, if the schema gets changed due to any configuration changes, it will not reset the next components however it will show an exclamation mark.

This is how the corresponding configuration, logic and results from each operator is validated while the pipeline is being designed.

Clone Pipeline

Clone pipeline functionality helps you create a new pipeline from an existing pipeline by giving it a new name. Typically when configuring the pipeline for newer possibilities, it is very useful to take an existing pipeline and clone it.

Select Clone Pipeline on the pipeline tile.

A pop up window with Pipeline configuration will open.

Edit the cloned pipeline’s configuration as per requirement, under the tab Pipeline.

Under the Component tab, you can edit the component’s connection details.

Provide a unique name to your cloned pipeline as shown below:

Below shown is the Component tab:

Data Preparation in Gathr

Data preparation is the process of collecting, cleaning and organizing data for analysis. Prepare the data with operations and expressions to generate customized data. Graphs and statistics are also used to present the prepared data.

To perform data preparation, configure a Data Source (For example, Kafka). Once the Data Source is configured and saved, you can run inspection on the columns of the schema that are reflected in the Data Preparation window. This allows you to build the pipeline while you are interacting with the data.

To perform Data Preparation, click on the eye icon of any component (Data Source, Processor or Emitter). A sub- section on the pipeline canvas opens with the component’s schema displayed in Data Preparation window.

In the example below, an Agriculture data file is uploaded. It holds information about crop, soil, climate, pests, season, average_yield, cost, high_production_state, crop_wastage and fertilizer.

By default, the data is displayed in Summary view with Profile Pane on the screen.

Data Pane: The schema takes the form of columns and is divided in records (as shown in the records). You can apply operations on an entry of a column or any possible combination, corresponding to which a new expression is added, which keeps creating your pipeline as in when you apply more actions.

The data preparation window has the following options:

Data preparation window has type2 more views: Summary View and Detailed View

Summary View shows the summary of number of records out of total records and also the columns in each record. It also shows the name of the current schema.

The Detailed View shows details of a selected column value and its corresponding rows.

Detailed View has eight sections. All the sections are displayed and explained below:

An important and elaborate section amongst the ones mentioned in the table above is Operations.

Operations

This option is at the top right of the column. The gear icon, which opens a sliding window, shows a list of operations (shown in image below) that can be performed on the respective column of the schema. Whenever any operation is applied on a column (s), a step is created and reflected in the left section of Data Preparation Window. Also, a processor is added to the pipeline, depending on the operation applied.

These data preparation processors are defined below in data preparation processors section.

a. Filter

Filter the values based on a set of criteria (shown in image below) such as, Equals, Contains, Regex match and so on. Custom filter can be used for a custom value.

b. Transforms

Filter the values based on a set of criteria (shown in image below) such as, Equals, Contains, Regex match and so on. Custom filter can be used for a custom value.

c. Missing Value Replacement:

Replace the missing or null values with either Literal or Expression value.

Literal: Replaces Null and/or empty string values with a specified string literal.

Expression: Replaces Null and/or empty string values with a specified expression value.   

d. Analyze

Under Analyze, you can view the Null values of the selected column.

Under Statistics, you can view the mathematical statistic/value of the entire column in the form of:

l Minimum

l Maximum

l Mean

l Median

l Standard Deviation

l Mode

l Distinct

l Sum

l Range

e. Pivot

Pivot operation is a kind of aggregation where type1 (or more) of the grouping columns has its distinct values transposed into separate columns.

Original Data

Pivot Operation in Gathr

l Pivot column – C

l Grouping fields – A, B

l Pivot values – small, large

l Type of aggregation – Sum

l Aggregation Column - D

Pivoted Data:

g. Group By

You can apply aggregation on a column using Group By operation and save the output in a field with or without grouped columns.

h. Rename Column

Rename the column name.

i. Create New Column

Create a new column using this filter.

j. Remove Column

Remove the selected column.

Data Preparation Processors

Data Preparation operations add following processors in your data pipeline:

• Expression Filter

• Expression Evaluator

• Rename

• Select

• Drop

Data Preparation steps appear on left side of inspect window, as a sliding panel.

The data preparation operations when performed keep getting added to the data pipeline and are reflected in data preparation steps.

Even when data preparation steps are added manually in a pipeline, they are reflected in data preparation steps.

Expression Filter:

Expression Filter enables filter operations on the data column.

This processor gets automatically added on the canvas once you apply the filter operation.

For example, let us add the filter criteria as “crop” equals “cereals”.

Expression Evaluator

Expression Evaluator is used when you want to perform transform operations on the data columns. This processor gets automatically added on the canvas once perform any transform operation on the data columns.

Scheduling

To execute a workflow periodically, click on the Scheduling tab and select any of the following option to schedule.

To access the Scheduling Tab, go to the Data Pipeline home page and click on the ellipsis.

The user can schedule a Batch Data Pipeline in fixed intervals through the Scheduling option. The pipeline can be scheduled by using two options i.e., Normal Scheduler and Cron Scheduler

Normal Scheduler

To schedule a pipeline using the Normal Scheduler option, follow the below steps:

Cron Scheduler

The user can schedule a pipeline for the below mentioned time frames:

l Minutes

l Hourly

l Daily

l Monthly

l Yearly

If Monthly is selected, then you can choose from within the options shown below:

Note: A pipeline can only be scheduled/unscheduled while it is in the stopped stage.

SCHEDULE/UN-SCHEDULE: This action will Schedule/un-schedule any scheduled workflow/pipeline and mark all the coordinated actions as terminated that are in running or waiting state.

The History of the pipeline run can be viewed under the History tab:

Auto Inspect Subsequent Sections

Whenever a column is either modified or removed in a component, a message will appear asking whether you want to auto-update subsequent components or not. When you click on Yes, the pipeline auto-updates the subsequent components and will stale all those components that will get impacted by the modification or removal of a particular column from the previous component.

As shown, Expression Evaluator is being updated:

A Warning will be displayed for the components that fail auto-update operation:

Remove Stale Configuration

In a Join Processor, if any field is removed from the source dataset, then that field will be marked in Red. also in case the same field is used in any condition.

You can remove all red columns by clicking REMOVE STALE CONFIGURATIONS and in the projection pane of join processor.

Auto-connect Components

Drag, drop and connect a component with auto-connect feature.

Drag a component near an existing valid component, and as the dragged component gets closer to the available component on canvas, they are highlighted. Drop the component and it will be connected to the data pipeline.

For example: In the following image, RabbitMQ component was dragged near S3 and S3 is highlighted. Once RabbitMQ is dropped near S3, both the components will get auto-connected.

Version Control

Version control allows you to create a version of a data pipeline.

StreamAnalytix supports version control system with StreamAnalytix Metastore.

The pipeline versions will get saved in underlying database of StreamAnalytix.

A Working Copy is the current version of the copy or the first (0) version of the copy.

The pipeline editing is performed on the working copy, always.

Note: Any pipeline version once created becomes history and cannot be edited unless it is a working copy. If user tries to edit a version which is a working copy and switches back to a previous version, it overwrites the existing working copy from the selected version.

To create a version:

First way is when you re-save an edited pipeline, you get an option to Create Version.

Second way to enable version on your pipelines is when you edit and save the pipeline. Amongst the option of saving the pipeline is Create Version and Switch Version button.

Third option to create a version is by the pipeline tile.

Mention a description or a comment for the version and click on Create Version. Once you save the version, the last version on the pipeline changes from ‘Working Copy’ to Version 1.

When user selects any one of the options to create the Pipeline versions, there will be a notification displayed stating “Version added in Queue for creation” followed by “Version creation completed”.

A pipeline remains a working copy as long as no other version is created. Once you save a version, next to the title ‘Create Subsystem version’, is the notification of the next version that will be created.

Switch Version

If you want to switch the version of a pipeline, click on Switch Version on the pipeline editor page and choose a version. It will change the pipeline as per the selected version.

It is the Working Copy that is loaded to a newer version. Editing is always performed on the Working Copy of the pipeline.

NOTE: If you want to switch to an older version, save the current state as a version.

Download Version

Download Version from pipeline tile allows you to download a version of the pipeline.

Click on the Download arrow to download the pipeline.

Download/Upload Pipeline

Download Pipeline

A pipeline can be downloaded along with its component’s configurations such as Schema, Alert configuration, and Registered entities.

Upload Pipeline

A pipeline can be uploaded with its component configurations such as schema, alert definitions and registered entities.​

To upload a pipeline, click on the Upload Pipeline button on the Data Pipeline-home page.

If there is any duplicity in the pipeline components, a confirmation page will appearas shown below:

Click on Create Version button to see the Exported Pipeline Details page that provides the details of the pipeline and the environment from where it was downloaded.

Click on Overwrite button and the existing pipeline will be overwritten with the current configuration of the pipeline, which was uploaded.

To create a new pipeline, click New Pipeline.

Click on Proceed, and update the pipeline configuration details. Once the configuration is completed, upload the pipeline and it will get listed on the Pipeline page.

Update Pipeline Configuration

The next window has two tabs, Pipeline and Component.

Pipeline button has properties such as, Pipeline Name, Log Level, Yarn Queue, and HDFS User. Expand the pipeline configurations by clicking on More button (which appears on the next screen). More button allows you to configure Error Handler Configuration for the modified pipeline.

Click on the Component tab to update pipeline component configuration and connection details if required. Once done, click on the Upload button to upload the pipeline with the changes configured.

While uploading the pipeline, if the components used in the pipeline are not configured in the Workspace it is uploaded, then no connection will be available for that particular component.

This will not abort the upload process but the Start button of the pipeline tile will remain disabled until a connection is configured for the respective component.

Once a connection is created, update the pipeline and select a corresponding connection to play the pipeline.

New Pipeline button will allow to save the uploaded pipeline as a new pipeline.

First page will be the exported pipeline’s details page and after clicking on Proceed button, Pipeline configurations are displayed where the pipeline’s name and other configurations can be changed.

View Summary

This tab enables sending emails with the statistics of the data pipeline, to a user with customized details.

Click on Stats Notification from the pipeline tile of the Data Pipeline homepage.

This page allows you to enter the email address, on which you will receive the emails. Choose the duration of the emails, as show below:

The other tab Metrics shows all the metrics of the pipeline that will be sent in the email.

View Summary

Once a pipeline has been started and is in Active Mode, you can view its Summary.

To view the real-time monitoring graphs, click on the pipeline summary (go to the Data Pipeline page > Pipeline tile > click on the View Summary icon) as shown below:

To enable real time monitoring graphs, go to

Configuration >defaults >spark > EnableMonitoringGraphs and set its value to true

By default, this value is set to False.

In addition, you can view the Spark Job page from the application_1525…. link as shown below:

The View Summary tab provides the following Tabs:

Monitoring

Under the Monitoring Tab, you will find the following graphs, as shown below:

• Query Monitor

• Error Monitor

• Application Error

Query Monitor

The graph will be plotted in panels, these panels will be equal to the number of emitters used in the structure streaming pipeline. Each panel will have three graphs as per below discretion. These graphs can be seen through the view summary option.

Input vs. Processing Rate
The input rate specifies rate at which data is arriving from a source (Data Source) e.g. Kafka.

Processing rate specify the rate at which data from this source is being processed by Spark

Here number of input rows defines number of records processed in a trigger execution.
Batch Duration
Approximate time in to process this over all micro-batch(trigger) in seconds.

Aggregation State
Shows aggregation state of current streaming query. It shows information about operator query that store the aggregation state. The graph will be seen in case if pipeline has done some aggregation.

Continuous Integration Continuous Delivery

Gathr supports continuous integration and continuous delivery of pipeline, in form of test suites and by deploying pipelines on destination.

Continuous Integration

Continuous integration is done by executing test cases (as part of a test-suite) on the incremental state of a pipeline that ensures the pipeline is not impacted by new changes

Test Suite is a collection of Test Cases associated with a pipeline.

You may want to create a test case to verify multiple permutations on continuous data which will then be deployed as soon as they pass the tests.

How to create a test case?

A test case comprises of a set of source data files (one data file for each Data Source) and inspection outputs of each component in the pipeline. These inspection outputs of each component can be thought as the expected data for the component

Every test case will have its source data associated with each Data Source. When a test suite is executed, all its associated test cases are executed too. A test suite run is uniquely identified by a run id. When a test case is executed as part of a run id, each component is inspected again, and the output data can be referred as the actual data.

How a test case is evaluated to pass or fail?

A test case is passed if both the conditions mentioned below are fulfilled:

1. Number of records are the same for actual and expected data.

2. Every field value of each record in actual data matches with the expected data.

Let’s take an example to understand this.

Your source data may have integers, or some blank values and you may want to perform certain tests to make sure that the invalid or null values are omitted. These test cases can be created with different source data files to test.

Note: When you upload a source file via Upload Data, that file is inspected, and the output is saved. However, when you use, Fetch From Source on a Data Source, Gathr internally generates a source data file using the data from the source.

To create a test case, you need to configure, save and inspect the pipeline.

Create Test Case

Once the pipeline is saved and inspected, a test case can be created. Go to the menu on the data pipeline canvas, as shown below:

When you click on Test Suite, it opens a New Test Case windo. Click CREATE button.

To create a new test case, enter details in the New Test Case window fields, as shown below: