Skip to main content

​
Validate using data quality rules and policies

The DataQualityPlugin Interceptor validates Kafka producer records using Policies made up of: It works by evaluating every record produced to matching topics and taking action on violations. This is the recommended Interceptor to start with for data quality validation, and it is the same Interceptor that is deployed when you create data quality Policies in Console. You should use Console when you want to reuse Rules and Policies, but you can also validate data quality using only Gateway. This guide explains how to deploy the Interceptor directly on Gateway without Console.
From our blog: Kafka policy enforcement How to move from 50-item policy checklists to automated, enforceable rules at the proxy layer.

​
Examples

​
Mark and report with dead letter queue

Mark violating records on the customer-transaction topic and send them to a dead letter queue. Records are not blocked and still reach the original topic.

​
Enforce schema ID

If your schema registry requires authentication, pass credentials using additionalConfigs. Avoid exposing secrets in your configuration with secured templates. Gateway will use its local environment variables to resolve at runtime.

​
Block records that violate a JSON schema

Block any record on the product topic that does not match the expected product schema. This is useful for defending against poison pill records from errant CLI tools or misconfigured producers that write malformed data to a topic, which can cause downstream consumers to crash. No schema registry is needed since the JSON schema is defined inline in the Rule.

​
Configuration

If you are deploying this Interceptor without Console, set consoleDeploymentId to any placeholder value such as "standalone". This field will be made optional in a future version.

​
Rules

Each Rule in the rules map has a type field that determines how the record is validated. The Interceptor supports four Rule types.

​
CEL expression

Evaluate a CEL expression against the record. The expression has to return a boolean: true means the record passes, false means it violates the Rule. Available variables in CEL expressions:
  • value — the deserialized record value (access nested fields with dot notation, for example value.customer.email)
  • key — the record key
  • headers — a map of header names to values
  • topic — the topic name
  • partition — the partition number
  • offset — the record offset
Example CEL expressions:

​
JSON schema

Validate the record value against a JSON schema definition. This is especially helpful when your producer team wants to enforce standards on produced data without breaking downstream consumers by introducing a schema registry.

​
ENFORCE_AVRO built-in Rule

Verify that the record value is Avro-encoded: it has a schema ID prepended, the schema ID exists in the schema registry and the schema is of type Avro. Requires schemaRegistryConfig to be set.

​
ENFORCE_SCHEMA_ID built-in Rule

Verify that the record value has a valid schema ID prepended and that the schema exists in the schema registry. Requires schemaRegistryConfig to be set.

​
Data quality policy schema registry

A schema registry is only required if your topics use schema-encoded data. If your topics contain plain JSON, you can omit the schemaRegistryConfig field entirely.

​
Actions

The Interceptor supports three actions that can be combined: If both block and mark are enabled, only blocking is applied.
When blocking is enabled, a single invalid record causes the entire record batch for that partition to be rejected. This means valid records in the same record batch are also blocked. If you need to preserve valid records while flagging violations, use mark instead of block.

​
Mark header format

When marking is enabled, the conduktor.dataquality.violations header contains a JSON object:
Multiple Interceptors targeting the same topic will each append their Policy to this header.

​
Dead letter queue

When dlq is true and dlqTopic is set, the Interceptor sends violating records to the dead letter topic with the following headers: The Interceptor sends the violating records to the dead letter topic regardless of whether the action is block or mark. The block action will block the entire record batch that contains the violating record, but even so, only the violating record will be sent to the dead letter topic.

​
Handle blocked batches

When block is enabled, the producer receives an INVALID_RECORD error (Kafka error code 87). This error is non-retriable and affects all records accumulated in the same record batch as the violating record. Because the Kafka producer client will not automatically retry the request, block should only be used when it is imperative that violating data not be written to disk. The error message returned to the producer identifies the violated Rules, for example: Data quality Rule 'email-rule' violated. Data quality Rule 'age-rule' violated. If custom message fields are set on the Rules, those messages are used instead.

​
Handle producer errors

When testing in a development environment or favoring a “fail fast” approach to data quality validation, you should throw the InvalidRecordException in the record send callback to stop the application. If the Kafka producer doesn’t have an error callback or the callback is written to simply log the error and continue, then all records in the same record batch as the violating record will be lost. If you decide a violating record must be blocked, but do not want to lose other passing records in the record batch, you must handle this error explicitly. One valid approach is:
  1. Create a separate Kafka producer object with batch.size = 1 and linger.ms = 0 to only send records one at a time.
  2. Catch InvalidRecordException so each record in the failed batch is passed to the separate producer in a different thread and retried. Use .flush() to ensure each record is sent by itself.
  3. If this second send fails with the same exception, then we know this is a bad record and you can log the error message and continue.
The tradeoff to this approach is that it will not preserve record ordering. The record ordering is affected in two ways:
  • The records in the failed batch will be retried out of order because the callback is executed asynchronously
  • The main producer will continue to produce even while records in the failed batch are being retried

​
Violation reporting

When report is true, the Interceptor writes Avro-encoded records to an internal Kafka topic named:
All records on this topic are wrapped in a DataQualityEvent Avro envelope. Two event types are written:

​
Violation events

The Interceptor writes a DataQualityViolation event each time a record fails validation. The maxNumberOfViolationReportPerSecond configuration controls the rate at which the Interceptor writes violation events to this topic to prevent flooding under high violation volumes.

​
Evaluation count events

The Interceptor writes a DataQualityChecksCount event periodically (not per-record) with aggregated counts of all records evaluated, including records that passed validation. This provides the denominator for calculating violation rates. Each event contains a list of counts grouped by cluster, topic, Policy and Rule:
This topic is also used by Conduktor Console to display violation metrics and history.

​
Validate data using SQL-like checks

Conduktor Gateway offers a DataQualityProducerPlugin Interceptor that uses a SQL-like language to assert data quality before it’s being produced. Records in the topic from the FROM clause have to match the WHERE clause for the statement in order to be considered valid. This is particularly useful if your data is plain JSON with no schema but it can also be applied to AVRO, Protobuf data.

​
Example

You have a topic for orders with records in this form:
You may want to ensure that the:
  • id is a valid UUID format
  • amount_cents is a positive integer and not too large
  • currency is one of your accepted currencies
  • order_date is in ISO 8601 format
This can be asserted with:
In the statement, the list of selected fields is actually ignored - the important parts are the FROM clause (specifying the topic that the policy is applied to) and the WHERE clause, which specifies the condition data must meet in order to be considered valid. i.e. If the select returns something, the record is valid. If it returns no results, the record is considered invalid. SELECT [ignored!] FROM [topic name] WHERE [field filter criteria] Only one topic can be specified in the FROM clause (joins will be ignored), and the topic name is matched explicitly (no regexp support). If a record does not match the WHERE clause, it will be rejected. There are a variety of options for this described in the actions below. Fields are assumed to be from the value of the record. The Interceptor currently supports values in JSON, AVRO and Protobuf formats.
Topic names with dash - characters in them must be double quoted, as the dash is not a valid character for a SQL name. E.g. for a topic our-orders you would need to use:SELECT * FROM "our-orders" WHERE ...Nested fields can be accessed as expected with dot notation in the WHERE clause, e.g.:address.street = 'Electric Avenue'

​
WHERE clause

If you specify a field name in the WHERE clause that doesn’t exist in the record, the condition will always fail and the record will always be considered invalid. Fields in the WHERE clause have to exist in a record for it to be considered valid. The WHERE clause supports a subset of SQL operations:
  • The operators =, >, >=, <, <=, <> and REGEXP (RegExp MySQL Operator)
  • When providing more than one condition in the WHERE clause, only the AND
  • The IN clause is not supported, but can be approximated with a RegExp
  • By default, the fields in the WHERE clause are looked up from the value in the record. You can also filter by other parts of the record using the syntax below:
    • Record key (it also supports encoded keys which require a schema registry lookup):
      • Record key as string: - .. WHERE record.key = 'some thing'
      • Record key as schema: .. WHERE record.key.someValue.someChildValue = 'some thing'
    • Partition: .. WHERE record.partition = 1
    • Timestamp: .. WHERE record.timestamp = 98717823712
    • Header: .. WHERE record.header.someHeaderKey = 'some thing'
    • Offset: .. WHERE record.offset = 1

​
Actions for invalid data

The policy acts on produce requests from Kafka clients which means it will often deal with a batch of multiple records spread over multiple topics and partitions. The policy can apply different effects to each request batch based on its configuration.

​
Dead letter topic

If a dead letter topic service is configured for Gateway, you can optionally supply a topic name for this policy to use for any records which are considered invalid. This topic will be created with the default config for your Kafka setup. Any record that the policy considers invalid, is written to the dead letter topic and has some headers added for audit purposes. Please note that this is also done in the AUDIT_LOG_ONLY mode, even though the records in this mode are still written to the “real” topic. The generation of these headers can be disabled with the addErrorHeader configuration parameter (defaults to true). If no deadLetterTopic is configured for the policy, no messages will be written out in this manner.

​
Audit log

Any policy violation is logged in the configured Gateway audit log. This is currently logged at the batch level for each topic in the produce request. There’s no per record audit - it identifies that a policy breach occurred for the produce request and identifies the tenant, username and client IP for the request.

​
Configuration

Schema registry
If you don’t supply a basicCredentials section for the AWS Glue schema registry, the client we use to connect will instead attempt to find the connection information it needs from the environment and the credentials required can be passed this way to Gateway as part of its core configuration. Find out more from AWS documentation . Read our blog about schema registry .

​
Action

​
Example

​
Validate schema payload

To enable your Kafka consumers to confidently and independently access data, ensure that all records sent through your Kafka system conform to an agreed structure. Records with missing or invalid schemas can cause application outages, as consumers may be unable to process the unexpected record format. Moreover, the use of schemas can broadly only assert structural correctness of data and a level of compatibility for those structures. When it comes to the values in a record often all you can assert is a basic data type (integer, string, double etc.). This means that using a valid schema solves some concerns around data quality, there are other concerns to be dealt with in a bespoke or distributed manner across all the clients of a given data type. Finally, while correct structure can be enforced in a Kafka ecosystem at a client level - each client needs to ensure that it knows and follows the expectations for the data. You cannot prevent one client correctly writing AVRO to a topic, while another one writes plain JSON to the same topic. If one client doesn’t know the rules, it can’t follow them.

​
Enforce centralized policies

The schema validation Interceptor provides functionality that can be configured once in your Kafka system on the source for data (a topic), to ensure that:
  • All records produced to Kafka have a schema set
  • The record contents adhere to that schema
  • The fields (values) in any given record comply to business validation rules you have set in the schema
This policy provides a centralized enforcement of the validation of these rules at the point of write to Kafka. This enforcement cannot be bypassed or ignored by a client, so provides a strong guarantee that data actually written in to Kafka does match your rule set. Read our blog about schema registry .

​
How does the Policy Work?

The policy operates on Produce Requests made to Kafka, and will inspect the entire batch of records in a request. Based on its setup, it performs various checks and then will take an action if it finds any problems. The first important thing to note is that the Policy will do nothing if there is no Audit Log configured for Gateway (as it does not want to silently reject any data). So for the policy to work at all, you must have the Audit Log configured. Next point of note is that the policy will only check the value for a Kafka record, and does not currently support checking the key or headers for a record. The core config values for the policy itself are:
  • topic : the topic/s to apply the rule to
  • schemaIdRequired : whether records must/must not have a schema assigned to them
  • validateSchema : whether the policy should check if the data for the record matches the schema found for the record.
  • action : what to do if a problem is found
There are three levels of check you can apply:

​
Action

If any problems are found, the policy will take an action as configured. The action can be set to one of:
  • BLOCK → If any records in the batch fail the policy checks, record the problems in the audit log and then return an error to the client failing the entire batch. No records are written to Kafka at all if at least one of the records in the batch is considered invalid.
  • INFO → In this mode the data is always written to Kafka whether it passes the checks or not - but any problems found recorded in the audit log.
  • THROTTLE → If any records in the batch fail the policy checks, the data is still written to Kafka but the request will be throttled with time = throttleTimeMs, forcing the client to back off. Any problems found are recorded in the audit log.

​
Dead letter topic

If a dead letter topic service is configured for Gateway, then you can optionally supply a topic name for this policy to use for any records which are considered invalid. This topic will be created with the default config for your Kafka setup. Any record which the policy considers invalid is written to the dead letter topic, and has some headers added for audit purposes. Please note that this is done in the AUDIT_LOG_ONLY mode also, even though the records in this mode are still written to the “real” topic. The generation of these headers can be disabled if required, through the addErrorHeader configuration parameter (defaults to true). If no deadLetterTopic is configured for the policy, then no messages will be written out in this manner.

​
Configuration

The full configuration topics for the policy are as below.

​
Schema registry

If you do not supply a basicCredentials section for the AWS Glue schema registry, the client we use to connect will instead attempt to find the connection information is needs from the environment, and the credentials required can be passed this way to Gateway as part of its core configuration. More information on the setup for this is found in the AWS documentation . Read our blog about schema registry .

​
Example

Schema registry with secured template

​
Schema payload validations

When configured to do so, the schema validation Interceptor supports validating the value in a Kafka record against a specific set custom constraints for AvroSchema records. This is similar to the validations provided by JsonSchema, such as: For fields in an Avro schema, you can specify specific constraints on what is considered a correct value. These rules operate on the specific fields value only.
  • INT, LONG, FLOAT, DOUBLE: minimum, maximum, exclusiveMinimum, exclusiveMaximum, multipleOf
  • STRING: minLength, maxLength, pattern, format
  • ARRAY: maxItems, minItems
Current supported String format values:
  • byte, date, time, date-time, duration, uri, uri-reference, uri-template, uri, email, hostname, ipv4, ipv6, regex, uuid, json-pointer, json-pointer-uri-fragment, relative-json-pointer
This Interceptor also supports further validating elements from the whole payload against specific custom constraints - or Metadata Rules - using an expression based on the CEL Common Expression Language format. This provides a means to define more advanced rules dependent on multiple values in a record.

​
Metadata rule

​
JSON schema example

In JSON schema, constraints and rules are defined directly in the schema. Here’s an example that includes various validations:

​
Avro schema example

In Avro, constraints and rules are defined directly in the schema. Here’s an example that includes various validations: