Pulsar in 2026: 5 Things After 4 Months of Use
After 4 months with Pulsar: it’s decent for small teams, but a hassle for bigger projects.
Context
I’ve been using Pulsar since January 2026 as part of a project aimed at streamlining our data processing tasks. My team consists of five developers, and we’ve been working on a medium-scale application that processes large datasets daily. Every day, we handle around 10GB of data, running complex algorithms and generating reports that inform business decisions. I wanted to see if Pulsar could help us make this process more efficient.
What Works
One of the standout features of Pulsar is its ability to handle real-time data streaming. For instance, we’ve managed to set up a system that ingests data in real-time from various sources, processing it on-the-fly. The built-in dashboards provide a clear visualization of data flow and processing times, allowing us to keep a close eye on performance metrics. The ability to set alert thresholds for data processing errors has been incredibly beneficial, as we receive notifications before issues escalate.
Another feature that caught my attention is the integration with popular data storage solutions. We migrated a significant chunk of our data from AWS S3 to Pulsar’s native storage, which has resulted in faster read/write times. In tests, we noticed an average decrease in data retrieval time from 120ms to 85ms. That’s a noticeable difference when running batch jobs.
Moreover, Pulsar’s support for multi-tenancy has been useful. We’re able to separate different projects within the same environment without worrying about data leakage or performance hits.
What Doesn’t
Now for the fun part: the downsides. Pulsar’s documentation? It’s a mess. I spent hours trying to troubleshoot a problem with message acknowledgment failures. I kept getting this error:
Error: Message acknowledgment failed. Possible reasons: timeout or network issue.I even had to reach out to their support team, which was not the fastest experience. When I finally got a response, it was vague and didn’t really solve my issue. It felt like I was trying to decipher a foreign language.
Another glaring issue is the performance under load. We tried scaling up our processing and faced significant slowdowns. During peak hours, we observed latency spikes of up to 300ms, which is unacceptable for real-time applications. This led to a backlog in message processing, which in turn caused delays in data availability. The thing is, we were using a beefy server with plenty of resources—so it wasn’t a hardware issue.
Overall, the learning curve can be steep. I spent too much time fumbling around with configurations instead of writing actual code. Trust me, I’ve made my share of mistakes in the past, like the time I accidentally deleted a production database. Let’s just say panic mode kicked in pretty fast.
Comparison Table
| Feature | Pulsar | Kafka | AWS Kinesis |
|---|---|---|---|
| Real-time Processing | Yes | Yes | Yes |
| Throughput | Up to 1,000,000 messages/second | Up to 1,000,000 messages/second | Up to 200,000 messages/second |
| Error Handling | Moderate | Strong | Basic |
| Multi-tenancy Support | Yes | No | Yes |
| Documentation Quality | Poor | Good | Excellent |
The Numbers
Let’s cut to the chase: how does Pulsar stack up in terms of performance and costs? Here are some figures:
- Data Processed: 1,200GB/month
- Average Latency: 120ms (under regular load), 300ms (under peak load)
- Total Downtime: 3 hours in the last 4 months
- Cost: $0.08 per GB processed
In comparison, we previously used Kafka, which averaged around $0.06 per GB with a more consistent latency of 100ms. It’s clear that while Pulsar has some advantages, it doesn’t come cheap.
Who Should Use This
If you’re a small dev team working on data projects that require real-time processing and you don’t expect huge spikes in load, Pulsar might be a good fit. It’s particularly appealing if you need multi-tenancy capabilities. For example, if you’re managing several microservices that need data streaming, Pulsar can split them up effectively.
Solo developers building prototypes? It’ll serve you well. I’ve seen a few solo devs create impressive projects using Pulsar as their core data processing engine.
Who Should Not
If you’re part of a larger team—say over ten developers—working on complex applications, Pulsar might not be your best bet. The performance issues under load could cripple your workflow, and the support isn’t reliable enough for production-grade systems. If you’re relying on data availability, you might want to stick with Kafka or even AWS Kinesis.
Also, if you’re a newcomer to data streaming tech, the steep learning curve could be frustrating. Stick with simpler solutions until you’re more experienced.
FAQ
- Q: Is Pulsar open-source?
A: Yes, Pulsar is open-source, which is great for those wanting to modify the code. - Q: How does Pulsar handle message retention?
A: Pulsar allows you to configure retention policies based on your needs, which can be time-based or size-based. - Q: Can Pulsar integrate with other systems?
A: Yes, it offers connectors for various data sources and sinks, making integration easier. - Q: What programming languages does Pulsar support?
A: Pulsar has client libraries for Java, Python, Go, and more. - Q: Is there a community around Pulsar?
A: Yes, Pulsar has an active community, although the documentation isn’t always up to par.
Data Sources
1. Apache Pulsar Official Documentation
2. Apache Kafka Documentation
3. Personal benchmarks and real-world usage data.
Last updated May 13, 2026. Data sourced from official docs and community benchmarks.
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