capillaries

README

Capillaries

Capillaries is a data processing framework that:

• addresses scalability issues and manages intermediate data storage, enabling users to concentrate on data transforms and quality control;
• bridges the gap between distributed, scalable data processing/integration solutions and the necessity to produce enriched, customer-ready, production-quality, human-curated data within SLA time limits.

Why Capillaries?

	BEFORE	AFTER
Cloud-friendly	Depends	Can be deployed to the cloud within minutes; Docker-ready
Data aggregation	SQL joins	Capillaries lookups in Cassandra + Go expressions (scalability, parallel execution)
Data filtering	SQL queries, custom code	Go expressions (scalability, maintainability)
Data transform	SQL expressions, custom code	Go expressions, Python formulas (parallel execution, maintainability)
Intermediate data storage	Files, relational databases	on-the-fly-created Cassandra keyspaces and tables (scalability, maintainability)
Workflow execution	Shell scripts, custom code, workflow frameworks	RabbitMQ as scheduler, workflow status stored in Cassandra (parallel execution, fault tolerance, incremental computing)
Workflow monitoring and interaction	Custom solutions	Capillaries UI, Toolbelt utility, API, Web API (transparency, operator validation support)
Workflow management	Shell scripts, custom code	Capillaries configuration: script file with DAG, Python formulas

Getting started

On Mac, WSL or Linux, run in bash shell:

git clone https://github.com/capillariesio/capillaries.git
cd capillaries
./copy_demo_data.sh
docker compose -p "test_capillaries_containers" up

Wait until all containers are started and Cassandra is fully initialized (it will log something like Created default superuser role 'cassandra'). Now Capillaries is ready to process sample demo input data according to the sample demo scripts (all copied by copy_demo_data.sh above).

Navigate to http://localhost:8080 to see Capillaries UI.

Start a new Capillaries data processing run by clicking "New run" and providing the following parameters (no tabs or spaces allowed):

Field	Value
Keyspace	portfolio_quicktest
Script URI	/tmp/capi_cfg/portfolio_quicktest/script.json
Script parameters URI	/tmp/capi_cfg/portfolio_quicktest/script_params.json
Start nodes	1_read_accounts,1_read_txns,1_read_period_holdings

Alternatively, you can start a new run using Capillaries toolbelt by executing the following command from the Docker host machine, it should have the same effect as starting a run from the UI:

docker exec -it capillaries_webapi /usr/local/bin/capitoolbelt start_run -script_file=/tmp/capi_cfg/portfolio_quicktest/script.json -params_file=/tmp/capi_cfg/portfolio_quicktest/script_params.json -keyspace=portfolio_quicktest -start_nodes=1_read_accounts,1_read_txns,1_read_period_holdings

Watch the progress in Capillaries UI. A new keyspace portfolio_quicktest will appear in the keyspace list. Click on it and watch the run complete - nodes 7_file_account_period_sector_perf and 7_file_account_year_perf should produce result files:

cat /tmp/capi_out/portfolio_quicktest/account_period_sector_perf.csv
cat /tmp/capi_out/portfolio_quicktest/account_year_perf.csv

Monitoring your test runs

Besides Capillaries UI at http://localhost:8080, you may want to check out the stats provided by other tools.

Log messages generated by:

• Capillaries Daemon
• Capillaries WebAPI
• Capillaries UI
• RabbitMQ
• Cassandra with Prometheus jmx-exporter
• Prometheus are collected by fluentd and saved in /tmp/capi_log.

To see Cassandra cluster status, run this command (reset JVM_OPTS so jmx-exporter doesn't try to attach to the nodetool JMV process):

docker exec -e JVM_OPTS= capillaries_cassandra1 nodetool status

Cassandra read/write statistics collected by Prometheus available at: http://localhost:9090/graph?g0.expr=sum(irate(cassandra_clientrequest_localrequests_count%7Bclientrequest%3D%22Write%22%7D%5B1m%5D))&g0.tab=0&g0.display_mode=lines&g0.show_exemplars=1&g0.range_input=15m&g1.expr=sum(irate(cassandra_clientrequest_localrequests_count%7Bclientrequest%3D%22Read%22%7D%5B1m%5D))&g1.tab=0&g1.display_mode=lines&g1.show_exemplars=0&g1.range_input=15m&g2.expr=sum(irate(cassandra_clientrequest_localrequests_count%7Binstance%3D%2210.5.0.11%3A7070%22%7D%5B1m%5D))&g2.tab=0&g2.display_mode=lines&g2.show_exemplars=0&g2.range_input=15m&g3.expr=sum(irate(cassandra_clientrequest_localrequests_count%7Binstance%3D%2210.5.0.12%3A7070%22%7D%5B1m%5D))&g3.tab=0&g3.display_mode=lines&g3.show_exemplars=0&g3.range_input=15m

Further steps

Kubernetes

There is a Kubernetes deployment POC, but it may require some work: Minikube cluster setup, S3 buckets with proper permissions, S3-based Docker image repositories.

Blog at capillaries.io

For more details about this particular demo, see Capillaries blog: Use Capillaries to calculate ARK portfolio performance. To learn how this demo runs on a bigger dataset with 14 million transactions, see Capillaries: ARK portfolio performance calculation at scale.

Further introduction

For more details about getting started, see Getting started.

Deploy Capillaries at scale

Container-based deployments

Capillaries binaries are intended to be container-friendly. Check out the docker-compose.yml and Kubernetes deployment POC, these test projects may be a good starting point for creating your full-scale container-based deployment.

VM-based deployment

There is Capideploy project at https://github.com/capillariesio/capillaries-deploy which is capable of deploying Capillaries in AWS. Its a work in progress and it's not a production-quality solution yet.

Capillaries in depth

What it is and what it is not (use case discussion and diagrams)

Getting started (run a quick Docker-based demo without compiling a single line of code)

Testing

Toolbelt, Daemon, and Webapi configuration

Script configuration

Capillaries UI

Capillaries API

Glossary

Q & A

Capillaries blog

MIT License

(C) 2022-2024 KH (kleines.hertz[at]protonmail.com)