Ingesting Data
After installing LogScale on a server, you will want to put in place a system to feed data automatically into LogScale. This loading of information into LogScale is known as ingesting data. Configuring Data ingestion is an essential configuration step when you are setting up LogScale.
The following diagram provides an overview of the configuration flow to ingest data using LogScale.
Figure 92. Process graph
Ingesting Data
You can use different methods to ingest data depending on your requirements; OS, Log format and so on:
Ingesting FDR Data
LogScale can ingest Falcon Data Replicator (FDR) data into LogScale without having to configure log shippers. See Ingesting FDR Data.
Ingesting AWS S3 Bucket Data
LogScale can now ingest and parse AWS S3 bucket data. See Ingest Data from AWS S3.
S3 Ingest was introduced in v1.120.0
Log Shippers
LogScale is able to ingest data from a wide range of log shippers, Log shippers use the Ingest API to send one or more logs to LogScale. A log shipper can handle multiple logs, multiple log types, manage the log storage on disk, and pre-process the logs before sending them to LogScale. Log shippers are covered in more details in Log Shippers.
Listeners
Ingest listeners are a great way of shipping data to LogScale through raw sockets, using either UDP or TCP. See Ingest Listeners.
LogScale Ingest Tokens
A repository can have one or more ingest tokens associated with it. Ingest tokens are used with the Ingest API to enable data to be routed to the right repository, and to associate a parser. See Ingest Tokens.
LogScale API
The Ingest API can be used directly or through one of LogScale's APIs or software libraries. See the Ingest API reference page for more information. For a list of supported software, see the Software Libraries in the Appendix.
Parsing data
Parsing the data that is ingested enables the information to be tagged, specific fields and elements of the log data to be extracted, and enables an additional level of detail. The use of a parser also enables the type of the data and fields extracted to be configured, supporting metrics, graphing and dashboards. See Parsing Data.
In most cases you will want to use a log shipper or one of the LogScale platform integrations. If you are interested in getting some data into LogScale quickly, see Use Case: Ingesting Application Logs.
LogScale is optimized for live streaming of events in real time. If you ship data that are not live, you must observe some basic rules so that the resulting events are stored in LogScale as efficiently as if they had been received live. See Backfilling Data.