# S3, GCS and Azure Datalake source connectors are ignoring objects

**URL:** <https://ask.lenses.io/t/s3-gcs-and-azure-datalake-source-connectors-are-ignoring-objects/303>\
**Category:** Apache Kafka Connectors\
**Tags:** kafka-connect\
**Created:** [October 22, 2024, 10:09am UTC](https://ask.lenses.io/t/s3-gcs-and-azure-datalake-source-connectors-are-ignoring-objects/303 "2024-10-22T10:09:51Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![stheppi](https://dub1.discourse-cdn.com/flex017/user_avatar/ask.lenses.io/stheppi/32/30_2.png) [@stheppi](https://ask.lenses.io/u/stheppi)\
**Post date:** [October 22, 2024, 10:09am UTC](https://ask.lenses.io/t/s3-gcs-and-azure-datalake-source-connectors-are-ignoring-objects/303/1 "2024-10-22T10:09:52Z")

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I’m noticing that the s3 source connector is not actually grabbing _all_ data in the bucket for some reason. It seems nearly half is being ignored and never makes it into the topic.

An example of a file in the bucket would be named: 2024/49/prod-events-1-2024-10-04-00-00-00-30c458b5-f08d-4ca1-98a7-6583e7dfe46b and within this file there can be a few hundred JSON events separated by a new line.  
our config:

```auto
  "connector.class": "io.lenses.streamreactor.connect.aws.s3.source.S3SourceConnector",
  "connect.s3.source.partition.search.interval": "60000",
  "connect.s3.source.partition.search.continuous": "true",
  "connect.s3.kcql": "insert into event_pipeline.raw select * from ourbucketname STOREAS `json`",
  "tasks.max": "10",
  "connect.s3.aws.auth.mode": "Default",
  "name": "events-s3-source",
  "connect.s3.aws.region": "us-east-1",
  "value.converter": "org.apache.kafka.connect.storage.StringConverter",
  "connect.s3.source.partition.search.recurse.levels": "1"
}

```

The second directory in the full structure is where I try to partition things into 60 different possible partitions. 00-59, this is simply the current second of the timestamp when the event is created. Kinesis Firehose sets this prefix. So my thought was to use this in order to be able to scale out to multiple tasks (currently 10). So I think I’m well enough over-provisioned for this setup. We’re only talking about 15k events per minute

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<div class="post-metadata">

**Author:** ![stheppi](https://dub1.discourse-cdn.com/flex017/user_avatar/ask.lenses.io/stheppi/32/30_2.png) [@stheppi](https://ask.lenses.io/u/stheppi)\
**Post date:** [October 22, 2024, 10:12am UTC](https://ask.lenses.io/t/s3-gcs-and-azure-datalake-source-connectors-are-ignoring-objects/303/2 "2024-10-22T10:12:44Z")

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It looks like your configuration is using the default bucket object ordering, which is alphanumeric. This is generally the most efficient approach, as it takes advantage of S3’s built-in ability to list objects from a watermark in a lexicographical order. However, if your objects arrive at different times, some may be skipped if they appear before the last processed watermark in the lexicographic order.

From the object names you’re using, it seems this might be happening in your case. To resolve this, you should change the ordering by setting `connect.s3.ordering.type` to `LastModified`. This configuration ensures that objects are processed based on their timestamps instead. You can find more details in our documentation [here](http://docs.lenses.io/connectors/kafka-connectors).

Please note that using `LastModified` requires the connector to list all objects and sort them by their modification time, which impacts performance. And with growing number of objects the impact would only grow.