Curated SQL Views and Metrics for the Near Blockchain.
What's Near? Learn more here
- [PREREQUISITE] Download Docker for Desktop.
- Create a
.env
file with the following contents (note.env
will not be commited to source):
SF_ACCOUNT=zsniary-metricsdao
SF_USERNAME=<your_metrics_dao_snowflake_username>
SF_PASSWORD=<your_metrics_dao_snowflake_password>
SF_REGION=us-east-1
SF_DATABASE=NEAR
SF_WAREHOUSE=DEFAULT
SF_ROLE=PUBLIC
SF_SCHEMA=DEV
- New to DBT? It's pretty dope. Read up on it here
Run the follow commands from inside the Near directory (you must complete the Getting Started steps above^^)
make dbt-console
This will mount your local near directory into a dbt console where dbt is installed.
make dbt-docs
This will compile your dbt documentation and launch a web-server at http://localhost:8080
/models
- this directory contains SQL files as Jinja templates. DBT will compile these templates and wrap them into create table statements. This means all you have to do is define SQL select statements, while DBT handles the rest. The snowflake table name will match the name of the sql model file.
/macros
- these are helper functions defined as Jinja that can be injected into your SQL models.
/tests
- custom SQL tests that can be attached to tables.
CHAINWALKERS.PROD.NEAR_BLOCKS
- Near blocks
CHAINWALKERS.PROD.NEAR_TXS
- Near txs
Blocks and transactions are fed into the above two Near tables utilizing the Chainwalkers Framework. Details on the data:
- This is near-real time. Blocks land in this table within 3-5 minutes of being minted.
- The table is a read-only data share in the Metrics DAO Snowflake account under the database
FLIPSIDE
. - The table is append-only, meaning that duplicates can exist if blocks are re-processed. The injested_at timestamp should be used to retrieve only the most recent block. Macros exist
macros/dedupe_utils.sql
to handle this. Seemodels/core/blocks.sql
or/models/core/txs.sql
for an example. - Tx logs are decoded where an ABI exists.
CHAINWALKERS.PROD.NEAR_BLOCKS
- Near Blocks
Column | Type | Description |
---|---|---|
record_id | VARCHAR | A unique id for the record generated by Chainwalkers |
offset_id | NUMBER(38,0) | Synonmous with block_id for Near |
block_id | NUMBER(38,0) | The height of the chain this block corresponds with |
block_timestamp | TIMESTAMP | The time the block was minted |
network | VARCHAR | The blockchain network (i.e. mainnet, testnet, etc.) |
chain_id | VARCHAR | Synonmous with blockchain name for Near |
tx_count | NUMBER(38,0) | The number of transactions in the block |
header | json variant | A json queryable column containing the blocks header information |
ingested_at | TIMESTAMP | The time this data was ingested into the table by Snowflake |
CHAINWALKERS.PROD.NEAR_TXS
- Near Transactions
Column | Type | Description |
---|---|---|
record_id | VARCHAR | A unique id for the record generated by Chainwalkers |
tx_id | VARCHAR | A unique on chain identifier for the transaction |
tx_block_index | NUMBER(38,0) | The index of the transaction within the block. Starts at 0. |
offset_id | NUMBER(38,0) | Synonmous with block_id for Near |
block_id | NUMBER(38,0) | The height of the chain this block corresponds with |
block_timestamp | TIMESTAMP | The time the block was minted |
network | VARCHAR | The blockchain network (i.e. mainnet, testnet, etc.) |
chain_id | VARCHAR | Synonmous with blockchain name for Near |
tx_count | NUMBER(38,0) | The number of transactions in the block |
header | json variant | A json queryable column containing the blocks header information |
tx | array | An array of json queryable objects containing each tx and decoded logs |
ingested_at | TIMESTAMP | The time this data was ingested into the table by Snowflake |
Data in this DBT project is written to the NEAR
database in MetricsDAO.
This database has 2 schemas, one for DEV
and one for PROD
. As a contributer you have full permission to write to the DEV
schema. However the PROD
schema can only be written to by Metric DAO's DBT Cloud account. The DBT Cloud account controls running / scheduling models against the PROD
schema.
When conducting work please branch off of main with a description branch name and generate a pull request. At least one other individual must review the PR before it can be merged into main. Once merged into main DBT Cloud will run the new models and output the results into the PROD
schema.
When creating a PR please include the following details in the PR description:
- List of Tables Created or Modified
- Description of changes.
- Implication of changes (if any).
- Learn more about dbt in the docs
- Check out Discourse for commonly asked questions and answers
- Check out the blog for the latest news on dbt's development and best practices