Near DBT Project

Curated SQL Views and Metrics for the Near Blockchain.

What's Near? Learn more here

Setup

[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_DEV
SF_WAREHOUSE=DEFAULT
SF_ROLE=PUBLIC
SF_SCHEMA=SILVER

New to DBT? It's pretty dope. Read up on it here

Getting Started Commands

Run the follow commands from inside the Near directory (you must complete the Getting Started steps above^^)

DBT Environment

make dbt-console This will mount your local near directory into a dbt console where dbt is installed.

DBT Project Docs

make dbt-docs This will compile your dbt documentation and launch a web-server at http://localhost:8080

Documentation is automatically generated and hosted using Netlify.

Project Overview

/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.

Background on Data

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. See models/core/blocks.sql or /models/core/txs.sql for an example.
Tx logs are decoded where an ABI exists.

Table Structures:

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

Target Database, Schemas and Tables

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.

Branching / PRs

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).

More DBT Resources:

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

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