InfluxDB vs. Scylla

InfluxDB is very good at storing monitoring metrics (e.g. performance data). InfluxDB is not the right choice if you need to store other data types (like plain text, data relations etc.).

Incentivized

Scylla is well suited for high-throughput scenarios where keyed data must be read or written with consistently low latency. It's less appropriate for use cases requiring relational queries, secondary indexes, or more structured data sets.

• Perfect handling telemetry data.
• Low latency, near real time.
• SQL-like language makes it easier to query.

Incentivized

• Low-latency reads
• CQL has a familiar syntax
• Parity with Cassandra
• Practical features

• GUI based administrator console

Incentivized

• Better documentation for best practices (e.g., how to effectively use connection pooling)

InfluxDB is a near perfect product for time series database engines. The relatively small list of cons are heavily outweighed by it's ability to just work and be a very flexible and powerful database engine. The community and support provided by the corporation are the only areas I have little experience.

Incentivized

Very easy-to-understand syntax--uses CQL (same as Cassandra), which has many similarities to standard SQL. There are some gotchas, however, that must be known during schema development.

We have worked with the InfluxDB support team a few times so far and it has been positive. Issues submitted are worked on promptly and we have good feedback.

Incentivized

The Scylla cloud support team is incredibly responsive and proactive.

To be honest, I didn't look at alternatives since InfluxDB performs very well if you can oversee the lack of security and HA features. But for all challenges, there is an easy solution which brings you forward (e.g. read load balancing can be achieved by using a common HTTPS load balancer).

Incentivized

Scylla has a quick learning curve (same as Cassandra) compared to other proprietary solutions like BigTable. It supports higher throughput and lower latency that other NoSQL databases like MongoDB, which sacrifice those features for more flexibility and unique features.

• Provided us a time series DB.
• With the SQL-like language, it is very easy to learn.
• Empowered us to keep track of our events.

Incentivized

• Addresses latency requirements of our platform