Getting Cricket In Times Square Running Without Losing Your Mind
I set this up last year for a project that needed real-time data aggregation from scattered sources, and honestly the documentation is thin enough that I'm writing this for my own benefit. The initial configuration took about three hours before it actually started behaving. Not terrible, not great. It is a lightweight orchestration layer that sits between your data pipelines and your visualization layer. It handles routing, throttling, and state reconciliation across multiple upstream feeds. People often confuse it with a full ETL tool, which it is not. It does not transform. It moves, queues, and occasionally patches things together before they hit your dashboard or database. The package is available on npm under the name cricket-times-square. Install it globally if you are running it as a service, or locally inside a project directory. I recommend the local install with a wrapper script, because the global binary sometimes conflicts with Node version managers if you are juggling multiple projects.
npm install -g cricket-times-square After installation, run cts init in your target directory. This generates a config file with sensible defaults. The defaults assume you are connecting to at least one WebSocket source and one HTTP polling endpoint. If your setup is simpler than that, you will need to edit the config anyway.
Configuration Basics
The config file lives at ~/.cts/config.json by default. It uses a layered structure where environment-specific overrides take precedence. Here is the core shape: Sources go in the inputs array. Each source needs a type, an endpoint, and a polling interval measured in milliseconds. The output section defines where reconciled data lands. Supported sinks are PostgreSQL, Redis, and a file-based JSON stream. Kafka support exists but is considered experimental and I have seen two people break their topics using it in production.
Get the Full Details
Running It
Start the daemon with cts start --daemon. This forks the process and writes a PID file to /tmp/cts.pid. You can monitor it with cts status, which returns uptime, queue depth, and any recent error counts. I keep a second terminal open with cts logs --follow during initial runs because the error messages are more useful than the status output when something goes wrong. This is the thing that got me first. When you feed Cricket In Times Square more than about 500 events per second from a single source without adjusting the buffer size, it starts dropping messages instead of queuing them. The default buffer is 1024 slots. I learned this the hard way when a client's event stream spiked during a promotional window and nearly a third of their data vanished for six minutes. The fix is to set the buffer parameter in your source config. For example:
"buffer_size": 8192, "backpressure_mode": "drop-oldest" The drop-oldest strategy keeps the queue moving rather than blocking everything behind a full buffer. There is also a block mode that pauses the source until space opens up, but that tends to cascade into timeout failures with whatever is feeding you events. Drop oldest is almost always the right call unless you have audit requirements that make losing any message unacceptable.
Debugging Connection Issues
If a source shows as connected but no data appears in your sink, check the reconciliation interval. The default is 30 seconds. That is fine for monitoring dashboards that update slowly, but it is terrible for anything that needs near-real-time accuracy. Set it to 2 or 5 seconds in your config and restart. The performance cost is negligible on modern hardware. Another thing nobody mentions: if you are using Redis as a sink and the data seems to disappear between restarts, make sure you have appendonly yes set in your Redis config. Cricket In Times Square does not implement its own persistence layer. It trusts the sink to handle durability. If Redis drops the data, Cricket In Times Square has already moved on and does not retry automatically.
When It Is The Wrong Tool
Do not use this if you need data transformation, schema validation, or complex routing logic based on content. It was never designed for that. If your pipeline requires filtering specific fields, merging records from multiple sources into one entity, or applying business logic before storage, you are better off putting a proper ETL layer in front of it. I have seen people try to force it into doing that by chaining scripts, and it just becomes a fragile mess that breaks whenever a source changes its payload format. Also, the monitoring interface is minimal. It gives you queue stats and connection status. There is no built-in alerting. If you need alerts, wire it up to something like Prometheus with the export endpoint it provides on port 9090, or write a simple wrapper script that checks the status output and sends a notification through your existing system.
Upgrade Path
Major version updates are rare but when they come they tend to shift the config format. Always back up your config and run cts migrate --dry-run before upgrading. It will tell you what fields changed and whether your current config is compatible. The migration itself is usually automatic but it writes a new config file and leaves the old one as a backup with a timestamp suffix.
