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Version: 10.9.x

CSV Fast Data Connector

This microservice allows you to fetch data from CSV files and deliver it with validation to the Fast Data.

In order to achieve this, CSV file specifications must be described in a ConfigMap in the Console by means of a JSON schema. In this way both validation of input data and custom mapping to projections are provided, ensuring the columns in the input file match the corresponding properties of associated projection.

Environment variables

NameRequiredDescriptionDefault value
HTTP_PORTfalsePort exposed by the service3000
LOG_LEVELtrueLevel to use for logging-
FILES_NAMES_REGEXEStruea comma-separated list of regex for filenames. Only files matching these regexes will be fetched from the bucket and processed.-
FILES_SERVICEtrueurl of the files-service used to download files-
PUSH_GATEWAY_SERVICEfalseurl of the push-gateway service-
KAFKA_BROKERStruecomma separated list of names for Kafka Brokers.-
KAFKA_GROUP_IDtruethe Kafka group ID.-
KAFKA_CLIENT_IDtruethe Kafka client ID.-
KAFKA_SASL_USERNAMEtruethe Kafka username.-
KAFKA_SASL_PASSWORDtruethe Kafka password.-
KAFKA_SASL_MECHANISMtruethe Kafka sasl authentication mechanism.-
SCHEMA_FOLDERfalsepath of the folder containing json schema definitions for the optional validation and mapping between CSV columns and the entity object properties./local/resources/
TOPIC_MAPtrueJSON string describing the mapping between the name of the projections and the name of the topic.-
LAUNCH_MECHANISMtruecould be either cronjob or polling.-
SCHEDULINGtruein case of a polling mechanism, the cronjob string for the scheduling of the import process.-

In case of duplicated CSV files (files with the same filename), the import process will consider them in the order they were written to the storage, with last written as the most recently updated one.


This service will simply look up for files in the bucket whose names match at least one of the FILES_NAMES_REGEXES, search for them in the bucket managed by the files-service and process them, validating each CSV line against the JSON schema provided in the folder SCHEMA_FOLDER.

To better manage the csv files and maintain a clean environment, it uses the multi-bucket functionality of the Files Service. It is therefore important to add the Files Service in the cluster and provide it a multi-bucket configuration.


Files Service supports multi-bucket configuration starting from the version v2.7.0.

Validation and mapping

JSON schema validation files are mandatory. In fact, without them it would be impossible to infer the data structure of the entities involved (e.g., the id of the future projection which is also the key of the Kafka message). JSON Schemas must be named after the projection to map, following this pattern: ${projectionName}_schema.json. A quick example: if the projection employees were to be mapped, its JSON schema file would be named employees_schema.json.

The schema has to describe an object type which properties correspond to the CSV columns in the file.

In order to bond a specific CSV column to a different name for the corresponding property in the projection, the property projectionProperty can be added too.


The csv-connector will map only those properties that are specified in the JSON Schema. Any other columns found in the csv file will not be taken into account.

For every object property in the JSON Schema, additional metadata info can be provided:

  • x-isKey: boolean specifying wether the property represents the id of the object;
  • x-projectionProperty: string describing the name that this property will have in the projection object (namely, the mapping between the csv column and the projection property);
  • x-isDelete: boolean that specifies if the object represented is to be deleted or not.

Let's say we want to map some CSV data describing the entity employees into its projection documents. We should first specify the filename regex expression in the FILES_NAMES_REGEXES environment variable for the csv source file (e.g, the regex (\.csv)$ for the source file employees.csv) and then insert this file in the path described in the SCHEMA_FOLDER variable. The mapping file employees_schema.json could be something like this:

"title": "employees",
"type": "object",
"properties": {
"id": {
"type": "string",
"x-isKey": true
"first_name": {
"type": "string",
"x-projectionProperty": "firstName"
"last_name": {
"type": "string",
"x-projectionProperty": "lastName"
"role": {
"type": "string",
"enum": ["developer", "manager", "system administrator"]
"salary": {
"type": "number"
"delete": {
"type": "boolean",
"x-isDelete" : "true"
"required": ["id", "first_name", "last_name", "role", "salary", "delete"],
"additionalProperties": false

The properties first_name, last_name and role are columns expected to be found in the CSV source file. The first two declare also the mapping with the projection properties. The third one specifies only the data validation (the name of the projection property will remain the same).

If we now want to add a json schema for another CSV file to process (e.g., for a new entity called offices), we need to add a file similar to employees_schema.json in the mapping folder. We should then make sure that the FILES_NAMES_REGEXES can still apply or add a new one to the list.

:::Caution The service will process only the files whose names match FILES_NAMES_REGEXES regex expression. If other mapping files are inserted in the mapping folder whose names aren't related to those csv files specified, they will not be taken into account and therefore no CSV files will be read for those specifications. :::

Kafka configuration

Details of Kafka topics must be provided too. In the environment variable TOPIC_MAP the link between the entities (namely, projections names) and the topics is specified.

Following the example of this page, TOPIC_MAP will follow this template:

"employees": "topic.employess",
"offices": "topic.offices"