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You can send Log events to Parseable via HTTP POST requests with data as JSON payload. You can use HTTP output plugins of all the common logging agents like FluentBit, Vector, syslog-ng, LogStash, among others to send log events to Parseable.

You can also directly integrate Parseable with your application via REST API calls.

Log Streams

Log streams are logical (and physical) collections of related log events. For example, in a Kubernetes cluster, you can have a log stream for each application, or a log stream for each namespace - depending on how you want to query the data. A log stream is identified by a unique name, and role based access control, alerts and notifications are supported at the log stream level.

To start sending logs, you'll need to create a log stream first, via the Console Create Log Stream button.


Schema is the structure of the log event. It defines the fields and their types. Parseable supports two types of schema - dynamic and static. You can choose the schema type while creating the log stream. Additionally, if you want to enforce a specific schema, you'll need to send that schema at the time of creating the log stream.

At any point in time, you can fetch the schema of a log stream on the Console or the Get Schema API.


Log streams by default have dynamic schema. This means you don't need to define a schema for a log stream. Parseable server detects the schema from first event. If the subsequent events (with new schema) and updates internal schema accordingly.

Log data formats evolve over time and users prefer a dynamic schema approach, where they don't have to worry about schema changes, and they are still able to ingest events to a given stream.


For dynamic schema, Parseable doesn't allow changing the type of an existing column whose type is already set. For example, if a column is detected as string in the first event, it can't be changed to int or timestamp in a later event. If you'd like to force a specific schema, you can set the schema while creating the stream.


In some cases, you may want to enforce a specific schema for a log stream. You can do this by setting the static schema flag while creating the log stream. This schema will be enforced for all the events ingested to the stream. You'll need to provide the schema in the form of a JSON object with field names and their types, with the create stream API call. Following types are supported in the schema: string, int, float, timestamp, boolean.


By default the log events are partitioned based on the p_timestamp field. p_timestamp is an internal field added by Parseable to each log event. This field specifies the time when the Parseable server received this event. Parseable adds this field to ensure there is always a time axis to the log events, so it becomes easier to query the events based on time.

You can also partition the log events based on a custom time field. For example, if you're sending events that contain a field called datetime (a column that has a timestamp in a valid format), you can specify this field as the partition field. This helps in speeding up the query performance when you're querying based on the partition field.

Note that the partition column of a log stream can be only be set at the time of log stream creation, and it is not possible to change it later. To set the partition column, you'll need to provide the column name in the log stream creation form.


Nested JSON objects are automatically flattened. For example, the following JSON object

"foo": {
"bar": "baz"

will be flattened to

"": "baz"

before it gets stored. While querying, this field should be referred as For example, select from <stream-name>. The flattened field will be available in the schema as well.

Batching and Compression

Wherever applicable, we recommend enabling log agent's compression and batching features to reduce the network traffic and improve ingestion performance. Max payload size in Parseable is 10 MiB (10485760 Bytes). The payload can contain single log event as a JSON object or multiple log events in a JSON array. There is no limit to number of batched events in a single call.


Correct time is critical in understanding the proper sequence of events. Timestamps are important for debugging, analytics, and deriving transactions. We recommend that you include a timestamp in your log events formatted in RFC3339 format.

Parseable uses the event received timestamp and adds it to the log event in the field p_timestamp. This ensures there is a time reference in the log event, even if the original event doesn't have a timestamp.

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