VictoriaMetrics Reviews

Apache Flink Reviews

VictoriaMetrics mentions (17)

• All you need is Wide Events, not "Metrics, Logs and Traces"

  Https://victoriametrics.com/ would definitely recommend anyone having performance issues with Prometheus to give VictoriaMetrics a try. - Source: Hacker News / 4 months ago

• Top 11 Grafana Alternatives in 2023

  VictoriaMetrics is primarily a time-series database designed for efficiently storing and querying time-series data. It is often used as a back-end data store for time-series data generated by monitoring systems like Prometheus. VictoriaMetrics excels at handling large volumes of time-series data, offering efficient storage and query capabilities. - Source: dev.to / 9 months ago

• InfluxDB CTO: Why We Moved from Go to Rust

  Not sure I follow since there are very competitive tools written in Go such as https://victoriametrics.com for an example in this space. - Source: Hacker News / 9 months ago

• Show HN: Homelab Monitoring Setup with Grafana

  I've found VictoriaMetrics all-in-one binary to be perfect size for home at the very least for metrics gathering. Supports Prometheus querying and few other formats for ingesting so any knowledge bout "how to get data into prometheus" applies pretty much 1:1 + their own vmagent is pretty advanced. Not related to company in any way, just a happy user. https://victoriametrics.com/. - Source: Hacker News / about 1 year ago

• Sizing Thanos for large Prometheus installation

  I am not part of the company in any way but please take a look at https://victoriametrics.com/. They are not very popular in the US but the product is easier to manage and more scalable than any other open source Prometheus replacements. High chances you wont even need scale-out design for your load. Source: about 1 year ago

Apache Flink mentions (30)

• Show HN: Restate, low-latency durable workflows for JavaScript/Java, in Rust

  Restate is built as a sharded replicated state machine similar to how TiKV (https://tikv.org/), Kudu (https://kudu.apache.org/kudu.pdf) or CockroachDB (https://github.com/cockroachdb/cockroach) since it makes it possible to tune the system more easily for different deployment scenarios (on-prem, cloud, cost-effective blob storage). Moreover, it allows for some other cool things like seamlessly moving from one log... - Source: Hacker News / 20 days ago

• Array Expansion in Flink SQL

  I’ve recently started my journey with Apache Flink. As I learn certain concepts, I’d like to share them. One such "learning" is the expansion of array type columns in Flink SQL. Having used ksqlDB in a previous life, I was looking for functionality similar to the EXPLODE function to "flatten" a collection type column into a row per element of the collection. Because Flink SQL is ANSI compliant, it’s no surprise... - Source: dev.to / about 1 month ago

• Show HN: An SQS Alternative on Postgres

  You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / about 2 months ago

• Top 10 Common Data Engineers and Scientists Pain Points in 2024

  Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / 3 months ago

• Choosing Between a Streaming Database and a Stream Processing Framework in Python

  Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 5 months ago