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click-runner

click-runner is a modular Python toolkit for loading data into ClickHouse from various external sources. It supports SQL query execution, CSV ingestion via ClickHouse URL engine, Parquet ingestion from S3 buckets, Google Drive CSV imports, and dedicated API ingestors for Mixpanel, CoW Protocol, Snapshot, and Discourse.

Purpose

Not all data in the Gnosis Analytics pipeline comes from blockchain nodes. click-runner handles the ingestion of external datasets:

  • Ember -- global electricity generation data used for ESG carbon footprint calculations
  • ProbeLab -- daily peer-to-peer network metrics (agent versions, peer distributions, crawl statistics)
  • Governance -- Snapshot proposals, votes, and followers plus Discourse forum topics, posts, and users (feeds the Governance Explorer mini-app)
  • Mixpanel -- product-analytics events and user profiles for Gnosis App and Gnosis Pay
  • CoW Protocol -- open orders and trade fees from the CoW API
  • Gnosis Pay on Celo -- card transfers and wallet events (crawlers_data.celo_gpay_*)
  • Google Drive -- ad-hoc CSV datasets shared via Drive
  • Administrative queries -- schema migrations, data maintenance, and custom SQL operations

Ingestion Modes

click-runner selects an ingestor via the --ingestor parameter: query, csv, parquet, gdrive, dune-execute-only, mixpanel, mixpanel-profiles, cow, snapshot, or forum.

Query Mode

Executes arbitrary SQL files against ClickHouse. Used for administrative tasks, schema updates, and custom transformations.

python run_queries.py --ingestor=query \
  --queries=queries/file1.sql,queries/file2.sql

CSV Mode

Imports data from CSV files using ClickHouse's built-in URL engine. The typical workflow involves three SQL files: table creation, data insertion (reading from a remote CSV URL), and optional optimization.

python run_queries.py --ingestor=csv \
  --create-table-sql=queries/ember/create_ember_table.sql \
  --insert-sql=queries/ember/insert_ember_data.sql \
  --optimize-sql=queries/ember/optimize_ember_data.sql

Parquet Mode

Imports data from Parquet files stored in S3 buckets. Supports three sub-modes:

Sub-mode Description Use Case
latest Import only the most recent file Daily incremental updates
date Import a file for a specific date Targeted backfills
all Import all available files Full historical load
# Import latest file
python run_queries.py --ingestor=parquet \
  --create-table-sql=queries/probelab/probelab_agent_semvers_avg_1d.up.sql \
  --s3-path=assets/agent_semvers_avg_1d_data/{{DATE}}.parquet \
  --table-name=crawlers_data.probelab_agent_semvers_avg_1d \
  --mode=latest

# Import file for a specific date
python run_queries.py --ingestor=parquet \
  --create-table-sql=queries/probelab/probelab_agent_semvers_avg_1d.up.sql \
  --s3-path=assets/agent_semvers_avg_1d_data/{{DATE}}.parquet \
  --table-name=crawlers_data.probelab_agent_semvers_avg_1d \
  --mode=date --date=2025-04-13

Google Drive Mode

Imports a CSV file shared on Google Drive by its file ID.

python run_queries.py --ingestor=gdrive \
  --create-table-sql=queries/new_source/create_table.sql \
  --table-name=crawlers_data.my_table \
  --file-id=<drive-file-id> --max-rows=1000000

Mixpanel Modes

Two ingestors cover Mixpanel: mixpanel exports raw events and mixpanel-profiles exports user profiles. Both write to the database configured via MIXPANEL_DATABASE (mixpanel_raw_events, mixpanel_raw_profiles, plus an ingestion-state watermark table).

# Daily incremental event export
python run_queries.py --ingestor=mixpanel --mixpanel-mode=daily --mixpanel-region=EU

# Historical backfill for a date range
python run_queries.py --ingestor=mixpanel --mixpanel-mode=backfill \
  --mixpanel-from-date=2026-01-01 --mixpanel-to-date=2026-06-30

--mixpanel-region selects the data-residency region (US, EU, IN); --mixpanel-event-filter restricts the export to a JSON array of event names.

CoW Mode

Fetches open orders and trade fees from the CoW Protocol API for owner addresses read from a source table (--cow-source-table, e.g. dbt.int_execution_cow_trades). Tables land in the database configured via COW_DATABASE.

Mode Behavior
daily Refresh orders for owners active in the last --cow-lookback-days (default 7)
backfill Fetch for all owners, optionally bounded by --cow-backfill-from
repair Re-fetch orders whose on-chain fills are missing from the target table

CoW API access requires TLS impersonation

The CoW API sits behind a CloudFront WAF that blocks plain Python TLS clients by JA3 fingerprint, even with a valid X-API-Key. The ingestor uses curl_cffi with Chrome browser impersonation to make requests. Keep this dependency in place when extending the ingestor.

Snapshot Mode (Governance)

Ingests Snapshot Hub GraphQL data — space metadata, proposals, votes, and followers — into the database configured via GOVERNANCE_DATABASE (snapshot_space, snapshot_proposals, snapshot_votes, snapshot_follows).

# Daily: refresh open + recently-closed proposals
python run_queries.py --ingestor=snapshot --snapshot-mode=daily --snapshot-vote-refresh-days=5

# Full backfill of all proposals and votes
python run_queries.py --ingestor=snapshot --snapshot-mode=backfill

Forum Mode (Governance)

Crawls the Discourse forum JSON API into forum_categories, forum_topics, forum_posts, and forum_users in the same governance database. Daily mode processes topics bumped since the stored watermark; backfill crawls everything (bounded by --forum-max-pages, 30 topics per page).

python run_queries.py --ingestor=forum --forum-mode=daily

Dune Execute-Only Mode

Triggers execution of dedicated Dune queries without ingesting results (used to refresh Dune-side materializations):

python run_queries.py --ingestor=dune-execute-only \
  --dune-execute-only-query-ids=123456,234567

Configuration

ClickHouse Connection

Variable Default Description
CH_HOST -- ClickHouse server hostname
CH_PORT 9000 ClickHouse native protocol port
CH_USER -- Authentication username
CH_PASSWORD -- Authentication password
CH_DB -- Target database name
CH_SECURE False Use TLS connection
CH_VERIFY False Verify TLS certificate

S3 Integration

Variable Default Description
S3_ACCESS_KEY -- AWS access key ID
S3_SECRET_KEY -- AWS secret access key
S3_BUCKET prod-use1-gnosis S3 bucket name
S3_REGION us-east-1 AWS region

Data Source URLs

Variable Description
EMBER_DATA_URL URL to the Ember electricity CSV data

Variable Substitution in SQL

SQL files support {{VARIABLE_NAME}} placeholders that are replaced with values from environment variables prefixed with CH_QUERY_VAR_. For example:

  • Environment variable: CH_QUERY_VAR_EMBER_DATA_URL=https://example.com/data.csv
  • In SQL: FROM url('{{EMBER_DATA_URL}}', 'CSV')

Docker Compose Services

The docker-compose.yml provides preconfigured services:

Service Mode Description
click-runner Any Generic service, accepts any --ingestor argument
ember-ingestor CSV Preconfigured for Ember electricity data
probelab-agent-semvers-ingestor Parquet Preconfigured for ProbeLab agent version data
# Run the Ember ingestor
docker-compose run --rm ember-ingestor

# Run the ProbeLab ingestor
docker-compose run --rm probelab-agent-semvers-ingestor

# Run arbitrary queries
docker-compose run click-runner --ingestor=query \
  --queries=queries/file1.sql

Scheduling

click-runner services are typically scheduled as daily cron jobs:

# Daily Ember data update at 2 AM
0 2 * * * cd /path/to/click-runner && docker-compose run --rm ember-ingestor

# Daily ProbeLab data update at 3 AM
0 3 * * * cd /path/to/click-runner && docker-compose run --rm probelab-agent-semvers-ingestor

A convenience script cron_setup.sh is included to automatically create these cron entries.

Adding New Data Sources

New CSV Source

  1. Create SQL files in queries/new_source/:
    • create_table.sql -- table schema definition
    • insert_data.sql -- INSERT using ClickHouse URL engine
    • optimize.sql (optional) -- post-load optimization
  2. Set the data URL as an environment variable
  3. Run with --ingestor=csv

New Parquet Source

  1. Create a table definition SQL file in queries/new_source/
  2. Run with --ingestor=parquet, specifying the S3 path pattern with {{DATE}} placeholder
  3. Add a Docker Compose service for convenient scheduling

New File Format

Extend the framework by creating a new ingestor class that inherits from BaseIngestor in ingestors/:

  1. Create ingestors/new_format_ingestor.py extending BaseIngestor
  2. Implement the ingest() method
  3. Register the new ingestor type in run_queries.py

Project Structure

click-runner/
├── run_queries.py          # Main CLI entry point
├── ingestors/
│   ├── base.py             # Abstract base ingestor
│   ├── csv_ingestor.py     # CSV ingestion logic
│   ├── parquet_ingestor.py # Parquet/S3 ingestion logic
│   ├── gdrive_ingestor.py  # Google Drive CSV imports
│   ├── mixpanel_ingestor.py            # Mixpanel raw events
│   ├── mixpanel_profiles_ingestor.py   # Mixpanel user profiles
│   ├── cow_ingestor.py     # CoW API orders + fees
│   ├── snapshot_ingestor.py# Snapshot governance data
│   └── forum_ingestor.py   # Discourse forum data
├── utils/
│   ├── s3.py               # S3 file discovery utilities
│   ├── db.py               # ClickHouse connection helpers
│   └── date.py             # Date parsing utilities
├── queries/
│   ├── ember/              # Ember electricity data SQL
│   ├── probelab/           # ProbeLab metrics SQL
│   ├── governance/         # Snapshot + forum table DDL
│   ├── celo_gpay/          # Gnosis Pay on Celo tables
│   ├── mixpanel/           # Mixpanel raw event tables
│   ├── mixpanel_profiles/  # Mixpanel profile tables
│   ├── cow/                # CoW API tables
│   └── dune/               # Dune imports
├── Dockerfile
└── docker-compose.yml

ClickHouse Table Schemas

The legacy CSV/Parquet sources below land in the crawlers_data database. The newer API ingestors write to env-configured databases instead: governance tables via GOVERNANCE_DATABASE, Mixpanel tables via MIXPANEL_DATABASE, and CoW tables via COW_DATABASE; Celo Gnosis Pay tables land in crawlers_data.celo_gpay_*.

Table: crawlers_data.dune_labels

Engine: MergeTree ORDER BY: (address, label, introduced_at)

Column Type Notes
address String Contract or EOA address
label String Human-readable label
introduced_at DateTime When the label was first seen
source LowCardinality(String) Label data source
Table: crawlers_data.dune_prices

Engine: MergeTree ORDER BY: (symbol, block_date)

Column Type Notes
block_date Date Price date
symbol LowCardinality(String) Token symbol
price Float64 Token price in USD
Table: crawlers_data.dune_bridge_flows

Engine: MergeTree ORDER BY: (bridge, source_chain, dest_chain, token, timestamp)

Column Type Notes
timestamp DateTime Bridge event timestamp
bridge LowCardinality(String) Bridge protocol name
source_chain LowCardinality(String) Origin chain
dest_chain LowCardinality(String) Destination chain
token LowCardinality(String) Bridged token
amount_token Float64 Amount in token units
amount_usd Float64 Amount in USD
net_usd Float64 Net USD flow
Table: crawlers_data.dune_gno_supply

Engine: MergeTree ORDER BY: (label, block_date)

Column Type Notes
label LowCardinality(String) Supply category label
block_date Date Snapshot date
supply Float64 GNO supply amount
Table: crawlers_data.ember_electricity_data

Engine: ReplacingMergeTree(version) ORDER BY: (Date, Area, Category, Subcategory, Variable)

Column Type Notes
Area String Country or region
ISO 3 code String ISO 3166-1 alpha-3 country code
Date Date Data date
Continent String Continent name
Category String Electricity data category
Subcategory String Electricity data subcategory
Variable String Measured variable
Unit String Measurement unit
Value Float64 Measured value
YoY absolute change Float64 Year-over-year absolute change
YoY % change Float64 Year-over-year percentage change
version DateTime Row version for deduplication
Table: crawlers_data.gpay_wallets

Engine: MergeTree ORDER BY: (SAFE_address, SAFE_createdAt)

Column Type Notes
SAFE_address String Safe wallet address
SAFE_createdAt DateTime Wallet creation timestamp