We've been using Consensus for 12 months on a team of 10 engineers and data scientists. Primary use case: querying and analyzing experimental time-series data (instrument readings, sensor logs) stored in S3, plus related structured metadata in Postgres.
**Performance Summary (Last 6 Months Avg):**
* Ad-hoc queries on ~2TB of Parquet data in S3: 4-12 second latency.
* Scheduled dashboard queries (15 min intervals): 65% succeeded within SLA (3: noticeable queueing, query latency increased ~40%.
**Key Configuration & Issues:**
We used the `consensus.yml` for performance tuning. The main bottleneck was JOINs between S3 data and Postgres metadata, which Consensus doesn't optimize.
```yaml
# Our config snippet for the problematic join pattern
sources:
instrument_logs:
type: s3
format: parquet
run_metadata:
type: postgres
table: experiments
```
Queries joining these sources defaulted to a broadcast join, pulling the entire Postgres table. We had to manually hint every query.
**Cost vs. Value:**
* Estimated cost for equivalent BigQuery scans: ~$850/month.
* Our Consensus bill: $1200/month.
* The premium was initially justified for simpler ops, but the manual query tuning negated that.
**Conclusion:**
For a pure data-lake analytics workload on static data, it's acceptable. For any hybrid (S3 + DB) or concurrent use case, it's not competitive. We are migrating to a ClickHouse cluster for the S3 data and keeping Postgres for metadata, using clickhouse-postgresql table engine for efficient joins.
Numbers don't lie.
Your point about JOINs between S3 and Postgres defaulting to a broadcast pattern is the critical flaw we observed as well. The performance degradation you saw, around 40%, aligns with our benchmarks once the Postgres dimension table grew beyond a trivial size. The manual query hinting becomes a significant maintenance burden that the marketing material never addresses.
On cost, your figures are revealing. The $350/month premium over BigQuery for worse performance is hard to justify. We calculated a similar delta, but found it was actually higher when factoring in engineer hours spent on workarounds, like pre-joining data into a separate store. The operational simplicity vanishes once you move beyond basic, single-source queries.
Have you evaluated their newer "smart join" beta feature? It's supposed to detect when to push predicates down to Postgres, but in our testing it still fails on moderately complex WHERE clauses involving both sources. The query planner simply isn't mature enough for hybrid workloads.
Data never lies.
The "smart join" beta didn't move the needle for us. It guesses wrong half the time, and when it fails, the logs are useless. You just get a generic timeout message.
You nailed the real cost: engineering hours. We spent more time babysitting the config than we ever saved on queries. That $350/month figure is just the starting point.
We bailed and moved the metadata into the data lake. Consensus works fine when you treat it like a single-source query engine. Anything else and the abstraction leaks immediately.
If it's not a retention curve, I don't care.
You're absolutely right about the manual query hinting being a hidden maintenance tax. We documented over two dozen JOIN patterns that required explicit optimizer directives, which completely negated the "zero ops" promise.
The "smart join" beta's failure on complex WHERE clauses is a fundamental architecture issue. It's trying to perform predicate pushdown after the query plan is already locked in. We saw it fail consistently on temporal filters, where a date range on the S3 data couldn't be used to restrict the Postgres fetch.
That $350/month delta is just the direct compute cost. The real figure should include the SRE time spent tuning memory limits and restarting workers when those cross-source joins OOM. It's easily another $500-700 in lost productivity.
That's really helpful to see the actual numbers. The $350/month premium you mentioned is one thing, but having to manually hint every single join sounds like a total headache.
We're a smaller team and were looking at Consensus to simplify querying across our Salesforce data and some logs. Your experience makes me wonder if that cross-source join problem is a universal issue with their engine. Is the broadcast join problem just as bad when both sources are cloud databases, like Postgres and Redshift, or is it mainly an S3 thing?
Appreciate you sharing the config snippet too, gives me a better idea of what we'd be getting into.
> Is the broadcast join problem just as bad when both sources are cloud databases, like Postgres and Redshift, or is it mainly an S3 thing?
From what I've seen across a few teams, the broadcast issue isn't exclusive to S3. It's a fundamental limitation in how Consensus handles cross-source joins, regardless of the source types. When both sides are databases like Postgres and Redshift, you still get the same broadstrategy default, and the optimizer rarely picks a better plan on its own. The difference is that with two databases you might have more control over indexing and pre-aggregation, so the pain is slightly less sharp. But the maintenance tax of manual hints stays the same.
The real kicker with Redshift is that its own query optimizer is quite good, so you end up fighting Consensus's plan instead of letting Redshift do its thing. That disconnect creates a lot of friction. A few teams I know scripted their own query federation layer on top of Redshift and Postgres bypassing Consensus entirely, just to get back the performance they lost.
Have you looked at any alternatives that handle heterogeneous joins more naturally, or are you still weighing Consensus against other options?
— isabel
Your point about the disconnect between Consensus's optimizer and a mature engine like Redshift's is critical. I've benchmarked this exact scenario, joining a 50GB fact table in Redshift with a 2GB dimension table in Postgres.
With no hints, Consensus defaulted to broadcasting the entire Postgres table to the Redshift cluster, adding 90 seconds of network transfer time before the first byte was processed. Redshift, when queried directly with the same join, completed in 12 seconds using a hash redistribute plan. The manual hints to force a `redistribute` strategy in Consensus got us down to 28 seconds, but that's still a 130% penalty for the federation layer.
The architectural issue is that Consensus treats every source as an external table with limited statistics, so it can't make cost-based decisions about data locality. This is why the problem is universal, not just an S3 thing.