Interana performs data imports (ingests) for Managed Edition customers. This document explains what's involved when your data is imported into Interana, and highlights things to consider to expedite the process. Click a link to jump directly to the topic.
Getting data into Interana
Importing data into an Interana cluster is a multi-step process, and is the same for all forms of ingest:
- one-time—a file or batch of files, ingested (data-at-rest) at a prescribed time.
- continuous—a pipeline (like a cron job) that pushes content into the cluster at a steady (data-in-motion) rate.
- streaming—a dynamic flow of live data received via HTTP from a cloud source.
The Interana import process, which can be broken down into the following phases:
- Download the data.
- Transform the data.
- Concatenate the data into batches.
- Schematize the batches.
- Distribute the data.
1. Download the data
Data is downloaded from your Amazon Web Services (AWS), Microsoft Azure, or other cloud platform environment. The structure of your data can affect how long it takes to ingest into Interana and be made available for queries. For more information, see What you should know about structuring your data.
2. Transform the data
JSON is the preferred format for Interana. Other formats, such as Apache Parquet, are supported. However, all other formats require transformation into JSON before being ingested into Interana. The format and organization of your data can affect how long it takes to process (transform). The more transformations that are required, the longer the ingest process will be. For more information, see What you should know about structuring your data.
3. Concatenate the data into batches
After the data is transformed, the files are concatenated into batches. Concatenating transformed files into batches is necessary prior to importing them into Interana. The size of your data files affects the resulting batches. Consider the following when structuring your data for smoother ingests:
- The default batch size is 1 GB.
- There is a limit to how many batches can be processed at one time.
- If the file size is too small, it takes longer to create batches.
- If the file size is too large, it takes longer to upload the batches.
- For optimum ingest times, it is recommended file sizes be between 10 to 25 MB.
4. Schematize the batches
The batches are schematized prior to import. This means each row of a batch is reviewed to determine the following:
- Is there a valid time stamp?
- Are there any new columns?
- Where should the data go, to which node and folder?
5. Distribute the data
After the batches are schematized, the data is sent to the appropriate nodes (data, string, etc), and the following occurs:
- The imported batch files are deleted from the import node.
- Records are placed in the Interana MySQL database documenting the import of each file. The records contain information on the original file size, the transformed file size, how many lines each file contains, which machine imported the file (if there are multiple import nodes), and if the import was successful or something went wrong.
Ways to increase import speed
Interana processes thousands of lines of data per second. There are several conditions that can influence Interana's processing speed. If you think your imports are lagging, consider making adjustments in the following areas.
The number of import nodes in your cluster—If you are importing a large volume of data and are not satisfied with the performance, you may need to increase the number of import nodes on your cluster. In general, it is recommended that you have 1 import node for every 4 data nodes. However, you can add ingest nodes to accommodate bulk imports, then remove the extra nodes after the bulk import is done. For more information, see Planning your Interana deployment and consult with your Interana Customer Success Manager.
The number of data and string nodes in your cluster—The main constraint for a data tier is disk space. However, beware of under-provisioning the CPU and memory resources as it can result in reduced performance. You should also consider your expected data retention, and adjust your data node configuration accordingly.
The main constraint for a string tier is disk space, though sufficient memory is important as well. SSDs are strongly recommended for the string tier. Use an odd number of string nodes, 3 or 5 such instances, and keep an eye on the disk usage. For more information, see Planning your Interana deployment and consult with your Interana Customer Success Manager.
The number of files and their size—The number of files you are importing can affect the time it takes for the data to be processed. A large number of files will take longer to transform and ingest.
File size is another important consideration for performance. If the files are too small, it will take longer to create batch files. If the files are too large, they take longer to transform and upload. The optimum file size is between 10 MB and 25 MB. For more information, see What you should know about structuring your data and consult with your Interana Customer Service Manager.
The size of the batch files—Batch file size can affect import speed. If batch files are too large, they take longer to process. Review the previous sections on concatenating data into batches and the number of files and their size, then consult with your Interana Customer Service Manager.
The number of batches processed at a time—The number of batch files that are processed at one time can affect the import speed. Performance can lag if the size of the batch files is large, as well as processing too many at once. Review the previous sections on concatenating data into batches and the size of batch files, then consult with your Interana Customer Service Manager.
The number of jobs running simultaneously—The number of overall jobs running at one time can affect import speed. Modifying the configuration of your cluster may be necessary to accommodate your data. For more information, see Planning your Interana deployment and consult with your Interana Customer Success Manager.