Processing Partial Valid Data in Real-time Application Integration

Introduction 

Siloed applications across enterprise needs to be interconnected to leverage data produced in other trusted data source applications. This helps different business units collaborate better, reuse, analyze, make informed decision and ultimately add great value to their offerings. The process of synchronizing data across applications is crucial exercise in enterprises and significant effort spent by teams on building robust integration. There are many types of integration mechanisms followed in industry today to build robust systems interacting with one another. Still there are challenges that need to be addressed. 

Data is tagged as invalid due to various reasons. It could happen in source, or in transit. Wrong way of processing, and network issues can make data invalid.  Such invalid data causes real-time data dependent systems fail in delivering data to customers in time. The turnaround time to fetch the valid data in further attempts delays the reports delivery and analytics that in turn slows down making informed decisions. Im proposing a solution in this article to mitigate this issue.

Widely Used Approach

Let us first understand the commonly used sync mechanism.  The main components that are part of any data synchronization are:

Data: Data is strategic asset to enterprises and comes in many forms. Structured, and unstructured being at the top of classification hierarchy, there are various other formats under this classification.
Source Application: A Data source which is trusted across enterprise
Middleware : Links two or more separate application. Provides common connection, orchestration, data transformation and mapping logic in integrating heterogeneous applications.
Target Application : Application that receive data from external sources and stores it.

Common approach in automated real-time integration system that suffers with time delay when encounters with invalid data :
Structured data is always subjected to validation testing for its each attribute when it migrated to new application. Receiving applications are designed and developed to reject whole record even single attribute in that record is invalid. Thus rejected record will come back to Source application where it has to be inspected and correction must be made either in automated way or manually. Manual intervention to correct the data consumes more time and error prone. This delay could make many reporting or analytics applications not depending on the invalid attributes loose time unnecessarily.

Step by step process:
1. Initial data load: Middleware receives the data and finds certain attribute empty
2. Handling invalid data: Middleware tries to find the data in other source systems if orchestration is enabled
3. Middleware processing: Middleware detects the record as invalid
4. Middleware rejects the data to source application
5. Data correction at source: Source application corrects the data and pushes it to middleware. This could take long time depending on the type of error. Most of the times, manual corrections require days to correct the data
6. Reconciled data in target system: Once corrected, data is pushed again to the middleware
7. Middleware validates and if valid, pushes the data to target application
8. Target application validates the data and if valid stores the data

Commonly used integration mechanism

Issues in Current Approaches

No integration is error free. Errors could happen due to various issues like computational factors, network issues, heterogeneity between applications and etc. These kind of errors can be minimized with careful design but cannot be eliminated as external factors play major role. Most of the integration systems build feature to handle invalid data as absence of such feature will lead to data mismatching, inaccurate computation, false reporting, mistrust, and huge cost. However, commonly followed standard approaches are not enough and still struggles with significant time delay while handling invalid data.

There are many challenges in achieving data sync across applications. Data is marked as invalid by receiving applications when it is incomplete, inconsistent, inaccurate or in wrong format. 

Not every attribute in the rejected record may be required for every kind of reporting or analytics applications. Many types of reporting and computing can still run without certain attributes. Practice of rejecting whole record is unnecessary and adds latency to real time data synchronization systems. This in turn negatively impacts data dependent business in various ways. 

My Solution Proposal 

If you agree with the above stated issues, read further to understand my proposal. My solution involves design changes in middleware and target applications to accept record even when partial attributes are found invalid. The specific record with error needs to be stored in its present state. Meanwhile the error is notified to the source application with appropriate error message. The missed or wrong format data is imputed by middleware and sent to the target application so that it can allow data store. Valid attributes of the same record can still be used in further data process, computation or display to the users. Only the error attribute is flagged. This flag indicates that the particular attribute cannot be used until it gets corrected from the source application. Once the new attribute value coming from source application is validated, flag will be removed for that attribute. This way of allowing partial dataset to reside and get processed will help valid data available to computation without any unwanted delay.

High level component view

Invalid data flagging design

The flagging design idea has to be implemented to hold the invalid data with indicators on it.

1. Flagging invalid record to indicate which attribute failed to pass validation rules

2. Format would be : Name of the attribute. In case of multiple attributes, all those attributes names can be mentioned as comma separated values.

3. Flagging record to indicate what reports or process still can run with such invalid record

4. Flag should be used by reporting or processing logic to see if the attribute required is flagged for incorrectness

Design implementation

Scenario 1: With empty value

1. Initial data load: Middleware receives the data and finds an attribute empty

2. Handling invalid data: Middleware tries to find the data from other source applications if orchestration is enabled

3. Middleware processing: If value for the attributer is not found, middleware imputes the data by looking at historical data or predicting using AI

4. Middleware processing: Middleware flags this record as invalid marking the attribute

5. Middleware pushes the data to target application

6. Target system processing : Target application accepts the data along with flag

7. Record stored in target application: Target application stores record along with flagging the invalid attribute. This flagging helps the data consuming component not to use attribute with invalid value in computation or reporting.

8. Reconciliation by middleware: Middleware in parallel send the invalid record back to the source application asking for correction of the invalid attribute

9. Data correction at source: Source application corrects the data and pushes it to middleware

10. Reconciled data in target application: This corrected data without flag is pushed to the target application

11. Data correction at target application: Target application validates the data and removes the flag for attribute

Scenario 2: With wrong format or inaccurate value

1. Initial data load: Middleware receives the data and finds an attribute is having wrong data format

2. Handling invalid data: Middleware flags this record as invalid marking the attribute

3. Middleware processing: Middleware pushes the data to target application

4. Target application processing : Target application accepts the data along with flag

5. Record stored in target application: Target application stores record along with flagging the invalid attribute. This flagging helps the data consuming component not to use attribute with invalid value in computation or reporting.

6. Reconciliation by middleware: Middleware sends the invalid record back to the source application asking for correction of the invalid attribute. Also in parallel tries to get the data through other data sources.

7. Data correction at source: Source application corrects the data and pushes it to middleware

8. Reconciled data in target application: This corrected data without flag is pushed to the target application

9. Data correction at target application: Target application validates the data and removes the flag for attribute. This allows the reporting and analytics functionality to utilize the attribute for further processing.  

Process Flow

Data Format

Conclusion

Following type of applications can benefit with the proposed design
1. Real time integration of applications

2. Integration of transactional systems where each data event needs to be captured and propagated to other system without delay
3. Structured data migration to other applications
4. Real time reporting and analytics applications which depends on external applications for data 

5. Applications interacting in Hub and Spoke design pattern

Should chaos engineering practice be made part of Devops CI/CD pipeline!!!

Answering this question with just Yes or No would be tough.  At first glance it looks like straight forward and possible. There are tools like Gremlin, Litmus or Chaos Monkey to simulate different types of failures, and then integrate these tools into your pipeline so that they are automatically run as part of your testing and deployment process. However, Its not as simple. Let us deep dive to see what are the challenges for including these fault injection experimentations into CD pipeline.

We need to start with basics of Chaos Engineering and the purpose of Devops to arrive at the detailed answer. We must start with the official definition:  

Chaos Engineering is the discipline of experimenting on a distributed system in order to build confidence in the system’s capability to withstand turbulent conditions in production.

Next we need to look at the principles. Principles of chaos engineering is well defined to help users understand the practice in detail. it includes:

Good understanding of the system

Including stakeholders

Formulating hypothesis

Preparing experiments

Planning for game day

Running experiments

Monitoring

Collecting metrics

Increasing blast radius 

Automating the fault injections using Devops pipeline would miss certain aspects of principles stated above. Monitoring the system behaviour on a scheduled day to run these experiments together with all the stakeholders is critical to the success of this practice. The learning that comes from live monitoring and analysis along with other stakeholders provides better perspective on the issues that are uncovered. It also helps in detecting the weak points in the system. 

Simple fault injections whose results are easily guessable without the need of monitoring may be considered as better candidate for CD pipeline. But there are not many such simple faults. Even simplest faults injected can manifest beyond imagination to become complex issue within the ecosystem. Monitoring system at every layer with every possible means is important while flats are being injected. Today’s distributed systems are complex with multiple layering and interdependent modules. Same yard scale cannot be used in every stage. 

Game day planning includes activities that cannot be automated easily. The fire drill concept, team gathering, and war room setup are some of the activities that must be experienced by every chaos engineering practitioner. These rehearsals build confidence to deal with the real life issues. Loosely based name sake automation could fail the preparedness objective. The real life chaotic surprises requires greater people collaboration skills to spring back at the earliest proving resiliency. Mere dependency on the rigid CD process without the possibility of eternal evolving would not yield better results. 

After every iteration of the fault injection exercise, teams must plan for increasing blast radius and learn more. Automation with same input criteria/parameters for every run would fail the objective of continuous improvement. Adding variable time in increasing manner is not tough automating. However, that requires complex logic inclusion and becomes unsustainable sooner than you think. Devops CD pipelines are not designed for handling such highly variable, time consuming and human intervention demanding process. 

Time is crucial parameter and with chaos engineering practice, every minute variation could increase possibilities of uncovering the new issue. The main motto of automation is to save on time. However, chaos engineering requires uses time as varying parameter to alter the characteristics of the fault injection and subject the system to fail using time as catalyst. Also, running time consuming process in Devops CD process is not recommended. This slows down the deployment process and fails the modularity aspect with increasing dependency. 

Conclusion

The above details with definition and principle establishes that this practice is nothing but detailed experimentation and not just mere testing that can be simply automated. Chaos engineering is about uncovering the inherent chaos in the system using unique approaches in every trial. Without the variable parameters, human collaboration, and real time monitoring, the process may not serve the real purpose of chaos engineering.