Lookup Files

Security Requirements and Controls

Lookup files are used to add additional context to data, enabling you to attach or replace text from events recorded in a repository when searched.

To add a lookup file, you create or import a CSV (comma-separated value) file and upload it to the repository.

These files can be used together with query functions to provide lookups and matching using the match() function.

The feature also works with the readFile() function for reading a file which is used as data input for your query.

The following operations are available:

For information on how Lookup files interact with the rest of the system, see Lookup Files Operations.

Creating a File

  1. Click Files → + New File → Create New.

  2. Specify a name for the file and then select either + Empty File to create an empty file to populate or From Package to use a template from a previously installed package.

  3. Click Create file.

  4. If you've created an empty file, click + to add rows and columns.

  5. Click Save to save the changes.

If you have many changes to make, editing a data table through the Files interface page can be tedious: click Export and then edit the table in a spreadsheet program or a simple text editor.

Note

Files larger than 100 MB cannot be viewed in the UI.

Create New CSV File

Figure 33. Create New CSV File


When a file is referenced in a query, a tab is shown in the Search page bearing the same name of the file. This file tab will display the file content as a Table widget. Alternatively, if the file cannot be queried, a download link will be presented instead. For example, executing the query:

logscale
groupBy([status])
| match(file="status_codes.csv", column="code", field="status", include=name)

will show a file table named status_codes.csv:

File Tab in Search View

Figure 34. File Tab in Search View


Uploading Files

  1. Go to the Files interface → + New file → Import files.

  2. Drag and drop your file or browse for the file to upload.

    You can upload a CSV file containing text like what you see below, which is essentially a lookup table that you can use for labels or value lookups.

    yaml
    userid,ip,username,region
1,"212.12.31.23","pete","EU"
2,"212.12.31.231","bob","EU"
3,"98.12.31.21","anders","EU"
4,"121.12.31.23","jeff","US"
5,"82.12.31.23","ted","AU"
6,"62.12.31.23","annie","US"
7,"122.12.31.23","joe","CH"
8,"112.11.11.21","alice","CH"
9,"212.112.131.22","admin","RU"
10,"212.12.31.23","wendy","EU"

    Once it has been uploaded, it will look like what you see in figure below.

    Import CSV File

    Figure 35. Import CSV File


    You would use such a data table together with the match() functions to add labels to the results of a search. Notice that the values are in quotes, except for the ones for userid, which are integers. See the Lookup API reference page for more information on this topic.

  3. Edit the data in the file editor table as you wish, and click + to add rows and columns. Clicking the tiny information icon next to the file name displays metadata info about the file (created by, time it was created, etc.)

  4. Once you have finished editing, click Save, or click Export if you wish to download the edited file.

Exporting or Deleting a File

Files can be managed by clicking the menu icon next to each file. You can either export or delete a file:

Manage CSV Files

Figure 36. Manage CSV Files


Warning

Deleting a file that is actively used by live queries will stop those queries.

Lookup Files Operations

When using Lookup files and match() functionality, consider the following:

  • Lookup files use server memory proportional to the size of the file on disk; at least as much and typically more. If you have a 1Gb lookup file it will take up at least 1Gb of memory on some, potentially all, hosts within the cluster. This requirement should be taken into account when uploading and sizing the nodes within the cluster.

  • From LogScale v1.108 on, content of the file is shared among all queries that uses match(), that is, the included columns that are common among match() functions can be reused among queries.

  • From 1.117 version on, whenever a file is updated, live queries and alert queries that use that file will seamlessly continue to run with the new updated file, thus making little difference if you have many small files to update or one large file. Since the file is swapped while the query is running, this also means that events can be queried with different versions of the file.

  • From LogScale v1.90, if you have large lookup files, wrap the uses of match() in saved queries rather than use them directly across multiple different queries to ensure you don't accidentally pass slightly different argument in different queries. However, due to an improved reuse of files introduced in LogScale v1.108, this practice is no longer necessary starting from that version.