Validate using data quality rules and policies
TheDataQualityPlugin Interceptor validates Kafka producer records using Policies made up of:
- custom Rules defined with CEL (Common Expression Language) expressions
- built-in Rules such as checking for Schema ID
- or a static JSON schema definition
Examples
Mark and report with dead letter queue
Mark violating records on thecustomer-transaction topic and send them to a dead letter queue. Records are not blocked and still reach the original topic.
- curl
- Conduktor CLI
Enforce schema ID
If your schema registry requires authentication, pass credentials usingadditionalConfigs. Avoid exposing secrets in your configuration with secured templates. Gateway will use its local environment variables to resolve at runtime.
- curl
- Conduktor CLI
Block records that violate a JSON schema
Block any record on topics prefixed withorders- that does not match the expected order 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.
- curl
- Conduktor CLI
Configuration
| Key | Type | Required | Default | Description |
|---|---|---|---|---|
policyName | String | Yes | A name to identify this Policy. Violation reports and marking headers reference this name. | |
topicsRegex | List<String> | Yes | List of regex patterns matching the topics to enforce. For example, ["^orders-.*$", "^payments$"]. | |
rules | Map<String, Rule> | Yes | A map of Rule names to Rule definitions. The Interceptor evaluates each Rule against every record. | |
block | Boolean | Yes | If true, reject the entire batch when any record violates a Rule. The producer receives an INVALID_RECORD error. | |
mark | Boolean | No | false | If true (and block is false), add a conduktor.dataquality.violations header to records that violate Rules. |
report | Boolean | Yes | If true, write violation events to an internal Gateway topic for monitoring. See violation reporting. | |
maxNumberOfViolationReportPerSecond | Integer | Yes | Rate limit for violation reports per second. A value of 10 is a good starting point. | |
dlq | Boolean | No | false | If true, send violating records to a dead letter topic. |
dlqTopic | String | No | The topic name to use as a dead letter queue. Required if dlq is true. | |
schemaRegistryConfig | Schema registry | No | Schema registry configuration. Required if your Rules use the ENFORCE_AVRO or ENFORCE_SCHEMA_ID types. | |
consoleDeploymentId | String | Yes | An identifier used to separate violation data across deployments. If you are not using Console, provide any placeholder value (for example, "standalone"). This field will be made optional in a future version. |
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 therules 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.
| Key | Type | Required | Description |
|---|---|---|---|
type | String | Yes | CEL |
expression | String | Yes | CEL expression to evaluate. |
message | String | No | Custom error message returned to the producer when blocking. Defaults to Data quality Rule '<rule-name>' violated. |
value— the deserialized record value (access nested fields with dot notation, for examplevalue.customer.email)key— the record keyheaders— a map of header names to valuestopic— the topic namepartition— the partition numberoffset— the record offset
| Use case | Expression |
|---|---|
| Email format | value.customer.email.matches(r”[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,}“) |
| UUID format | value.id.matches(r”^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$“) |
| Range check | value.customer.age >= 0 && value.customer.age <= 130 |
| Header validation | 'data-classification' in headers && headers['data-classification'] in ['C0', 'C1', 'C2', 'C3'] |
| Conditional required field | !value.customer.preferences.newsletter || (value.customer.preferences.preferred_language != null && value.customer.preferences.preferred_language != "") |
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.| Key | Type | Required | Description |
|---|---|---|---|
type | String | Yes | JSON_SCHEMA |
schema | String | Yes | A JSON schema definition (as a string). |
message | String | No | Custom error message returned to the producer when blocking. |
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. RequiresschemaRegistryConfig to be set.
| Key | Type | Required | Description |
|---|---|---|---|
type | String | Yes | ENFORCE_AVRO |
message | String | No | Custom error message. |
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. RequiresschemaRegistryConfig to be set.
| Key | Type | Required | Description |
|---|---|---|---|
type | String | Yes | ENFORCE_SCHEMA_ID |
message | String | No | Custom error message. |
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 theschemaRegistryConfig field entirely.
| Key | Type | Default | Description |
|---|---|---|---|
type | String | CONFLUENT | CONFLUENT for Confluent-like schema registries (including Redpanda) or AWS for AWS Glue. |
additionalConfigs | Map | Additional properties for security configuration. You can use environment variables as secrets. | |
| Confluent-like | |||
host | String | URL of your schema registry. | |
cacheSize | Integer | 50 | Number of schemas cached locally by this Interceptor. |
| AWS Glue | |||
region | String | AWS region (for example, us-east-1). | |
registryName | String | AWS Glue registry name. Leave blank for the default (default-registry). | |
basicCredentials | Object | AWS credentials (accessKey, secretKey). If omitted, credentials are resolved from the environment. |
Actions
The Interceptor supports three actions that can be combined:| Action | Behavior |
|---|---|
Block (block: true) | Reject the entire Kafka record batch for a partition if any record in it violates a Rule. The producer receives an INVALID_RECORD error (Kafka error code 87) with a message identifying the violated Rules. See handle blocked batches. |
Mark (mark: true) | Add a conduktor.dataquality.violations header to violating records. The header value is a JSON object mapping Policy names to arrays of violated Rule names. The Interceptor only applies marking when block is false. |
Report (report: true) | Write violation events to an internal topic for monitoring. See violation reporting. |
block and mark are enabled, only blocking is applied.
Mark header format
When marking is enabled, theconduktor.dataquality.violations header contains a JSON object:
Dead letter queue
Whendlq is true and dlqTopic is set, the Interceptor sends violating records to the dead letter topic with the following headers:
| Header | Description |
|---|---|
X-ERROR-MSG | Description of the violation including the Policy name, violated Rules and error details. |
X-TOPIC | The topic the record was originally destined for. |
X-PARTITION | The partition the record was intended for. |
X-POLICY | The Policy name that was violated. |
X-VIOLATED-RULES | Comma-separated list of violated Rule names. |
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
Whenblock 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 theInvalidRecordException 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:
- Create a separate Kafka producer object with
batch.size = 1andlinger.ms = 0to only send records one at a time. - Catch
InvalidRecordExceptionso 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. - 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 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
Whenreport is true, the Interceptor writes Avro-encoded records to an internal Kafka topic named:
DataQualityEvent Avro envelope. Two event types are written:
Violation events
The Interceptor writes aDataQualityViolation event each time a record fails validation.
| Field | Type | Description |
|---|---|---|
topic | String | The topic where the violation occurred. |
partition | Integer | The partition number. |
createdAt | Instant | Timestamp when the violation was detected. |
policy | String | The Policy name. |
violatedRules | List<String> | Rule names that were violated. |
gatewayClusterId | String | ID of the Gateway cluster. |
vCluster | String | Virtual Cluster name. |
serviceAccount | String | The service account that produced the record. |
clientId | String | Kafka client ID of the producer. |
errors | Map<String, String> | Error details per Rule (Rule name to error message). |
consoleDeploymentId | String | The deployment identifier. |
offset | Long or Object | The record offset. When the record was blocked, this contains batch metadata (batch size and offset within the batch) instead of the Kafka offset. |
actions | Object | The actions that were applied (block, report, mark, dlq). |
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 aDataQualityChecksCount 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:
| Count type | Fields | Description |
|---|---|---|
PolicyCheckCount | cluster, topic, policy, value | Total records evaluated for this Policy. |
RuleCheckCount | cluster, topic, policy, rule, value | Total records evaluated for this Rule. |
This topic is also used by Conduktor Console to display violation metrics and history.
Validate data using SQL-like checks
Conduktor Gateway offers aDataQualityProducerPlugin 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:idis a valid UUID formatamount_centsis a positive integer and not too largecurrencyis one of your accepted currenciesorder_dateis in ISO 8601 format
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
=, >, >=, <, <=, <>andREGEXP(RegExp MySQL Operator) - When providing more than one condition in the WHERE clause, only the
AND - The
INclause 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'
- Record key as string: -
- Partition:
.. WHERE record.partition = 1 - Timestamp:
.. WHERE record.timestamp = 98717823712 - Header:
.. WHERE record.header.someHeaderKey = 'some thing' - Offset:
.. WHERE record.offset = 1
- Record key (it also supports encoded keys which require a schema registry lookup):
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.| Action | Description |
|---|---|
| BLOCK_WHOLE_BATCH | If any records in the batch are invalid, then block the whole batch. The produce request will fail for the client in this case. |
| AUDIT_LOG_ONLY | For any records in the produce request which are invalid, record this in the audit log only. All records still are saved in Kafka |
| THROTTLE | If any records in the produce request are invalid, throttle the producer for a certain amount of time (throttleTimeMs). All records are still saved in Kafka. |
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 theAUDIT_LOG_ONLY mode, even though the records in this mode are still written to the “real” topic.
| Header | Message |
|---|---|
| X-ERROR-MSG | Message does not match the statement [ …] |
| X-TOPIC | The topic that the message was intended to be written to |
| X-PARTITION | The partition of the topic the message was intended to be written to |
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
| Key | Type | Description |
|---|---|---|
| statement | String | SQL statement |
| schemaRegistryConfig | Schema registry | Schema registry config |
| action | Action | Data quality producer action |
| deadLetterTopic | String | Dead letter topic |
| addErrorHeader | boolean (default true) | Adds the error information headers into dead letter topic |
| throttleTimeMs | int (default: 100) | Value to throttle with (only applicable when action is set to THROTTLE). |
Schema registry
| Key | Type | Default | Description |
|---|---|---|---|
type | string | CONFLUENT | The type of schema registry to use: choose CONFLUENT (for Confluent-like schema registries including OSS Kafka) or AWS for AWS Glue schema registries. |
additionalConfigs | map | Additional properties to map to specific security-related parameters. For enhanced security, you can hide the sensitive values using environment variables as secrets. | |
| Confluent Like | Configuration for Confluent-like schema registries | ||
host | string | URL of your schema registry. | |
cacheSize | string | 50 | Number of schemas that can be cached locally by this Interceptor so that it doesn’t have to query the schema registry every time. |
| AWS Glue | Configuration for AWS Glue schema registries | ||
region | string | The AWS region for the schema registry, e.g. us-east-1 | |
registryName | string | The name of the schema registry in AWS (leave blank for the AWS default of default-registry) | |
basicCredentials | string | Access credentials for AWS (see below section for structure) | |
| AWS credentials | AWS credentials configuration | ||
accessKey | string | The access key for the connection to the schema registry. | |
secretKey | string | The secret key for the connection to the schema registry. | |
validateCredentials | bool | true | true / false flag to determine whether the credentials provided should be validated when set. |
accountId | string | The Id for the AWS account to use. |
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
| Action | Description |
|---|---|
| BLOCK_WHOLE_BATCH | If one message is invalid, block the whole batch |
| AUDIT_LOG_ONLY | If messages are invalid, audit log only (all messages still are saved in Kafka) |
| THROTTLE | If messages are invalid, throttle the producer for a certain amount of time (throttleTimeMs) |
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
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 toschemaIdRequired: whether records must/must not have a schema assigned to themvalidateSchema: 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
| Setup | Effect |
|---|---|
schemaIdRequired = false | Ensures that no records have a schema! |
schemaIdRequired = true, validateSchema = false | Ensures that records have a valid schema set, and that schema exists in the schema registry. Does not check whether the value actually matches the schema though. |
schemaIdRequired = true, validateSchema = true | Ensures that records have a valid schema set, that schema exists in the schema registry and that the value in the record matches the schema. This includes any data validation rules in the schema (see below) as well as a structural check. |
Action
If any problems are found, the policy will take an action as configured. Theaction 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 theAUDIT_LOG_ONLY mode also, even though the records in this mode are still written to the “real” topic.
| Header | Message |
|---|---|
| X-ERROR-MSG | Description of the reason for the policy failure |
| X-TOPIC | The topic the message was intended to be written to |
| X-PARTITION | The partition of that topic the message was intended to be written to |
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.| Name | Type | Default | Description |
|---|---|---|---|
| topic | String | .* | Topics that match this regex will have the Interceptor applied |
| schemaIdRequired | Boolean | false | Records must/must not have schemaId |
| validateSchema | Boolean | false | If true, deserialize the record, validate the record structure and fields within the data itself. |
| action | BLOCK, INFO, THROTTLE | BLOCK | Action to take if the value is outside the specified range. |
| schemaRegistryConfig | Schema registry | N/A | Schema registry Config |
| celCacheSize | int | 100 | In memory cache size for cel expressions, balancing speed and resource use, optimize performance. |
| deadLetterTopic | String | Dead letter topic. Not used if this parameter is not set. | |
| addErrorHeader | Boolean | true | Add or not add the error information headers into dead letter topic |
| throttleTimeMs | int | 100 | Value to throttle with (only applicable when action is set to THROTTLE). |
Schema registry
| Key | Type | Default | Description |
|---|---|---|---|
type | string | CONFLUENT | The type of schema registry to use: choose CONFLUENT (for Confluent-like schema registries including OSS Kafka) or AWS for AWS Glue schema registries. |
additionalConfigs | map | Additional properties maps to specific security-related parameters. For enhanced security, you can hide the sensitive values using environment variables as secrets. | |
| Confluent Like | Configuration for Confluent-like schema registries | ||
host | string | URL of your schema registry. | |
cacheSize | string | 50 | Number of schemas that can be cached locally by this Interceptor so that it doesn’t have to query the schema registry every time. |
| AWS Glue | Configuration for AWS Glue schema registries | ||
region | string | The AWS region for the schema registry, e.g. us-east-1 | |
registryName | string | The name of the schema registry in AWS (leave blank for the AWS default of default-registry) | |
basicCredentials | string | Access credentials for AWS (see below section for structure) | |
| AWS Credentials | AWS Credentials Configuration | ||
accessKey | string | The access key for the connection to the schema registry. | |
secretKey | string | The secret key for the connection to the schema registry. | |
validateCredentials | bool | true | true / false flag to determine whether the credentials provided should be validated when set. |
accountId | string | The Id for the AWS account to use. |
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
- curl
- Conduktor CLI
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
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
Metadata rule
| Key | Type | Description |
|---|---|---|
| name | string | Rule name |
| expression | string | CEL expression for validation, must return BOOLEAN |
| message | string | Error message if payload not matches the expression with namespace message. represents for produced message |