LSIF is a file format for precomputed code intelligence data. It provides fast and precise code intelligence but needs to be periodically generated and uploaded to your Sourcegraph instance. LSIF is opt-in: repositories for which you have not uploaded LSIF data will continue to use the built-in code intelligence.
Precise code intelligence using LSIF is supported in Sourcegraph 3.8 and up.
For users who have a language server deployed, LSIF will take priority over the language server when LSIF data exists for a repository.
Follow our LSIF quickstart guide to manually generate and upload LSIF data for your repository. After you are satisfied with the result, you can upload LSIF data to a Sourcegraph instance using your existing continuous integration infrastructure, or using GitHub Action.
LSIF support is still a relatively new feature. We are currently working on validating that the feature remains responsive even with tens of thousands of repositories. To get started, we recommend you upload a smaller number of key repositories. Once you reach 50 to 100 repositories, we will be able to provide specific recommendations for ensuring stability and performance of your Sourcegraph instance.
Go to your global settings at https://sourcegraph.example.com/site-admin/global-settings and enable LSIF:
After uploading LSIF files, your Sourcegraph instance will use these files to respond to code intelligence requests (such as for hovers, definitions, and references). When LSIF data does not exist for a particular file in a repository, Sourcegraph will fall back to built-in code intelligence.
You may occasionally see results from basic code intelligence even when you have uploaded LSIF data. Such results are indicated with a tooltip. This can happen in the following scenarios:
Cross-repository code intelligence will only be powered by LSIF when both repositories have LSIF data. When the current file has LSIF data and the other repository doesn’t, the missing precise results will be supplemented with imprecise search-based code intelligence.
The following table gives a rough estimate for the space and time requirements for indexing and conversion. These repositories are a representative sample of public Go repositories available on GitHub. The working tree size is the size of the clone at the given commit (without git history), the number of files indexed, and the number of lines of Go code in the repository. The index size gives the size of the uncompressed LSIF output of the indexer. The conversion size gives the total amount of disk space occupied after uploading the dump to a Sourcegraph instance.
|Repository||Working tree size||Index time||Index size||Processing time||Post-processing size|
|bigcache||216KB, 32 files, 2.585k loc||1.18s||3.5MB||0.45s||0.6MB|
|sqlc||396KB, 24 files, 7.041k loc||1.53s||7.2MB||1.62s||1.6MB|
|nebula||700KB, 71 files, 10.704k loc||2.48s||16MB||1.63s||2.9MB|
|cayley||5.6MB, 226 files, 36.346k loc||5.58s||51MB||4.68s||11MB|
|go-ethereum||27MB, 945 files, 317.664k loc||20.53s||255MB||77.40s||50MB|
|kubernetes||301MB, 4577 files, 1.550m loc||1.21m||910MB||80.06s||162MB|
|aws-sdk-go||119MB, 1759 files, 1.067m loc||8.20m||1.3GB||155.82s||358MB|
The bulk of LSIF data is stored on-disk, and as code intelligence data for a commit ages it becomes less useful. Sourcegraph will automatically remove the least recently uploaded data if the amount of disk space falls below a configurable threshold. This value defaults to 10 GiB (10⨉2^30 = 10737418240 bytes), and can be changed via the
DBS_DIR_MAXIMUM_SIZE_BYTES environment variable.
To learn more, check out our lightning talk about LSIF from GopherCon 2019 or the introductory blog post: