The Stellar-ETL is a data pipeline that allows users to extract data from the history of the Stellar network.
Pay attention, it is very important to know if your modification to this repository is a release (breaking changes), a feature (functionalities) or a patch(to fix bugs). With that information, create your branch name like this:
release/<branch-name>feature/<branch-name>patch/<branch-name>
If branch is already made, just rename it before passing the pull request.
- Download the latest version of docker Docker
- Pull the latest stellar-etl Docker image:
docker pull stellar/stellar-etl:latest - Run the Docker images with the desired stellar-etl command:
docker run stellar/stellar-etl:latest stellar-etl [etl-command] [etl-command arguments]
- Install Golang v1.22.1 or later: https://golang.org/dl/
- Ensure that your Go bin has been added to the PATH env variable:
export PATH=$PATH:$(go env GOPATH)/bin - If using captive-core, download and install Stellar-Core v20.0.0 or later: https://github.com/stellar/stellar-core/blob/master/INSTALL.md
- Run
go install github.com/stellar/stellar-etl@latestto install the ETL - Run export commands to export information about the legder
- Clone this repo
git clone https://github.com/stellar/stellar-etl - Build stellar-etl with
go build - Build the docker image locally with
make docker-build - Run the docker container in interactive mode to run export commands.
$ docker run --platform linux/amd64 -it stellar/stellar-etl:latest /bin/bash- Run export commands to export information about the legder Example command to export ledger data
root@71890b878fca:/etl/data# stellar-etl export_ledgers --start-ledger 1000 --end-ledger 500000 --output exported_ledgers.txtNote: If using the GCS datastore, you can run the following to set GCP credentials to use in your shell
gcloud auth login
gcloud config set project dev-hubble
gcloud auth application-default login
Add following to docker run command to pass gcloud credentials to docker container
-e GOOGLE_APPLICATION_CREDENTIALS=/.config/gcp/credentials.json -v "$HOME/.config/gcloud/application_default_credentials.json":/.config/gcp/credentials.json:ro
Note: Instructions for installing gcloud can be found here
# Running all unit tests
go test -v -cover ./internal/transform
# Running an individual test
go test -v -run ^TestTransformAsset$ ./internal/transform# Running all integration tests
make int-test
# Running all integration tests and update golden files
make int-test-update
# Above essentially runs following:
docker-compose build
docker-compose run \
-v $(HOME)/.config/gcloud/application_default_credentials.json:/usr/credential.json:ro \
-v $(PWD)/testdata:/usr/src/etl/testdata \
-e GOOGLE_APPLICATION_CREDENTIALS=/usr/credential.json \
integration-tests \
go test -v ./cmd -timeout 30m -args -update=true
# Running an individual test
docker-compose build
docker-compose run \
-v $(HOME)/.config/gcloud/application_default_credentials.json:/usr/credential.json:ro \
-v $(PWD)/testdata:/usr/src/etl/testdata \
-e GOOGLE_APPLICATION_CREDENTIALS=/usr/credential.json \
integration-tests \
go test -v -run ^TestExportAssets$ ./cmd -timeout 30m -args -update=trueEvery command accepts a -h parameter, which provides a help screen containing information about the command, its usage, and its flags.
Commands have the option to read from testnet with the --testnet flag, from futurenet with the --futurenet flag, and defaults to reading from mainnet without any flags.
NOTE: Adding both flags will default to testnet. Each stellar-etl command can only run from one network at a time.
These commands export information using the Ledger Exporter output files within a specified datastore (currently datastore only supports GCS). This allows users to provide a start and end ledger range. The commands in this category export a list of everything that occurred within the provided range. All of the ranges are inclusive.
NOTE: The datastore must contain the expected compressed LedgerCloseMetaBatch XDR binary files as exported from Ledger Exporter.
| Flag | Description | Default |
|---|---|---|
| start-ledger | The ledger sequence number for the beginning of the export period. Defaults to genesis ledger | 2 |
| end-ledger | The ledger sequence number for the end of the export range | 0 |
| strict-export | If set, transform errors will be fatal | false |
| testnet | If set, will connect to Testnet instead of Pubnet | false |
| futurenet | If set, will connect to Futurenet instead of Pubnet | false |
| extra-fields | Additional fields to append to output jsons. Used for appending metadata | --- |
| captive-core | If set, run captive core to retrieve data. Otherwise use TxMeta file datastore | false |
| datastore-path | Datastore bucket path to read txmeta files from | ledger-exporter/ledgers |
| buffer-size | Buffer size sets the max limit for the number of txmeta files that can be held in memory | 1000 |
| num-workers | Number of workers to spawn that read txmeta files from the datastore | 5 |
| retry-limit | Datastore GetLedger retry limit | 3 |
| retry-wait | Time in seconds to wait for GetLedger retry | 5 |
NOTE: Using captive-core requires a Stellar Core instance that is v20.0.0 or later. The commands use the Core instance to retrieve information about changes from the ledger. More information about the Stellar ledger information can be found here.
As the Stellar network grows, the Stellar Core instance has to catch up on an increasingly large amount of information. This catch-up process can add some overhead to the commands in this category. In order to avoid this overhead, run prefer processing larger ranges instead of many small ones, or use unbounded mode.
Recommended resources for running captive-core within a KubernetesPod:{cpu: 3.5, memory: 20Gi, ephemeral-storage: 12Gi}
> stellar-etl export_ledgers --start-ledger 1000 \
--end-ledger 500000 --output exported_ledgers.txtThis command exports ledgers within the provided range.
> stellar-etl export_transactions --start-ledger 1000 \
--end-ledger 500000 --output exported_transactions.txtThis command exports transactions within the provided range.
> stellar-etl export_operations --start-ledger 1000 \
--end-ledger 500000 --output exported_operations.txtThis command exports operations within the provided range.
> stellar-etl export_effects --start-ledger 1000 \
--end-ledger 500000 --output exported_effects.txtThis command exports effects within the provided range.
> stellar-etl export_assets \
--start-ledger 1000 \
--end-ledger 500000 --output exported_assets.txtExports the assets that are created from payment operations over a specified ledger range.
> stellar-etl export_trades \
--start-ledger 1000 \
--end-ledger 500000 --output exported_trades.txtExports trade data within the specified range to an output file
> stellar-etl export_diagnostic_events \
--start-ledger 1000 \
--end-ledger 500000 --output export_diagnostic_events.txtExports diagnostic events data within the specified range to an output file
> stellar-etl export_ledger_entry_changes --start-ledger 1000 \
--end-ledger 500000 --output exported_changes_folder/This command exports ledger changes within the provided ledger range. Flags can filter which ledger entry types are exported. If no data type flags are set, then by default all types are exported. If any are set, it is assumed that the others should not be exported.
Changes are exported in batches of a size defined by the --batch-size flag. By default, the batch-size parameter is set to 64 ledgers, which corresponds to a five minute period of time. This batch size is convenient because checkpoint ledgers are created every 64 ledgers. Checkpoint ledgers act as anchoring points for the nodes on the network, so it is beneficial to export in multiples of 64.
This command has two modes: bounded and unbounded.
If both a start and end ledger are provided, then the command runs in a bounded mode. This means that once all the ledgers in the range are processed and exported, the command shuts down.
If only a start ledger is provided, then the command runs in an unbounded fashion starting from the provided ledger. In this mode, stellar-etl will block and wait for the next sequentially written ledger file in the datastore. Since the changes are continually exported in batches, this process can be continually run in the background in order to avoid the overhead of closing and starting new stellar-etl instances.
The following are the ledger entry type flags that can be used to export data:
- export-accounts
- export-trustlines
- export-offers
- export-pools
- export-balances
- export-contract-code
- export-contract-data
- export-config-settings
- export-ttl
These commands aid in the usage of Export Commands.
> stellar-etl get_ledger_range_from_times \
--start-time 2019-09-13T23:00:00+00:00 \
--end-time 2019-09-14T13:35:10+00:00 --output exported_range.txtThis command takes in a start and end time and converts it to a ledger range. The ledger range that is returned will be the smallest possible ledger range that completely covers the provided time period.
See https://github.com/stellar/stellar-etl/blob/master/internal/transform/schema.go for the schemas of the data structures that are outputted by the ETL.
This section covers some possible extensions or further work that can be done.
In general, in order to add new commands, you need to add these files:
export_new_data_structure.goin thecmdfolder- This file can be generated with cobra by calling:
cobra add {command} - This file will parse flags, create output files, get the transformed data from the input package, and then export the data.
- This file can be generated with cobra by calling:
export_new_data_structure_test.goin thecmdfolder- This file will contain some tests for the newly added command. The
runCLIfunction does most of the heavy lifting. All the tests need is the command arguments to test and the desired output. - Test data should be stored in the
testdata/new_data_structurefolder
- This file will contain some tests for the newly added command. The
new_data_structure.goin theinternal/inputfolder- This file will contain the methods needed to extract the new data structure from wherever it is located. This may be the history archives, the bucket list, a captive core instance, or a datastore.
- If working with captive core, the methods need to work in the background. There should be methods that export batches of data and send them to a channel. There should be other methods that read from the channel and transform the data so it can be exported.
new_data_structure.goin theinternal/transformfolder- This file will contain the methods needed to transform the extracted data into a form that is suitable for BigQuery.
- The struct definition for the transformed object should be stored in
schemas.goin theinternal/transformfolder.
A good number of common methods are already written and stored in the util package.
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