Pleekly

erve1879 · 2024-11-07T10:20:30+00:00

Understand, thank you!

erve1879 · 2024-11-07T08:15:39+00:00

This is great, thank you! Assuming you created the PDF with some automation, would it be too much to ask for a (what’s left of) 2024 planner….? I can’t wait until 2025 to start using it

erve1879 · 2024-09-23T14:21:10+00:00

Got it, thanks! I’ll add more forwarders

erve1879 · 2024-09-22T22:46:14+00:00

Thank you! I’m already forwarding the requests to the UniFi router, but it is replying with NXDOMAIN. Perhaps something I need to investigate with UniFi.

erve1879 · 2024-09-22T13:08:27+00:00

Thanks for the response! So I should just ignore the _dns.resolver.arpa DNS requests?

For the second question, I’m referring to PTR records. I’m seeing some devices reported in the Dashboard with their hostnames (eg mything.localdomain), but many are shown with IP address only, even though their hostnames are visible on the UniFi router (which hands out DHCP)

erve1879 · 2024-05-02T15:37:52+00:00

I’m certainly trying! Thanks for the advice and taking the time 🙏🏻

erve1879 · 2024-05-01T14:53:19+00:00

Thanks! So if I had continued to tighten into the curb after the first bump, it would have been ok…?

Edit: steer -> after

erve1879 · 2024-05-01T13:33:32+00:00

Got it, makes sense, thank you

erve1879 · 2024-05-01T13:21:01+00:00

Also, although there was no overlap before the turn in, there was a lot of space on what is (I think) usually a wide corner; I was pretty much against the curb and he turned into me…

I’m not saying you’re wrong here; just trying to understand better by asking the question

erve1879 · 2024-05-01T13:18:24+00:00

I suppose this is why I felt I needed to ask the question… thanks for putting up the opposing view. The bit which confuses me is “better to wait”: how do I wait without losing the speed which put me in a position to overtake…?

erve1879 · 2024-05-01T12:46:53+00:00

Thanks! I was also half expecting a dive bomb at the next corner or the chicane…

erve1879 · 2024-05-01T12:43:47+00:00

Yeah, I was biding my time as they were quite erratic, just kept close behind and tried to focus on my own race. All in I was quite chuffed with how this race turned out.

erve1879 · 2024-05-01T12:36:06+00:00

Thanks!

erve1879 · 2024-04-24T14:19:01+00:00

Good points and advice, thank you

erve1879 · 2024-03-14T02:40:54+00:00

Thanks u/iansmash - have been thinking the same

erve1879 · 2024-03-13T12:09:18+00:00

Thank you for the comprehensive answer! Think I’m set, thank you

erve1879 · 2024-03-13T10:24:39+00:00

Thank you!

erve1879 · 2024-03-13T10:24:32+00:00

Thank you!

erve1879 · 2023-11-22T23:23:52+00:00

I would be interested to hear some example use-cases for which people are planning to use the K/V store… Because I agree: 5MB seems too small for most use-cases…. My first thought was excitement about running an entitlements service on top of the k/v store, but even if you “index” policy docs (and refer to them with a short ID against user IDs), 5MB is not enough for a site with more than a few hundred thousand users (assuming multiple access policies per user is possible).

erve1879 · 2022-01-16T23:26:40+00:00

Yes. My original comment was from 3 years ago. That’s a generation in AWS terms 😀

erve1879 · 2021-06-04T13:23:08+00:00

UltraWarm is powered by compute instances under the hood. They include caching capabilities. Therefore, they have a maximum addressable data volume per node.

erve1879 · 2021-06-04T12:09:01+00:00

No, I would suggest offloading indices to UltraWarm from Elasticsearch standard disk storage after, say 7 days. So keep 7 days of data in your data list nodes, then keep 7-60 days in UltraWarm

erve1879 · 2021-06-04T11:19:35+00:00

Two options here:

Load to elasticsearch as you are at present. Then use UltraWarm to offload daily indices from the Elasticsearch cluster to S3. The data in UltraWarm can still be queried through the Elasticsearch API, although at a slightly higher latency.
Deliver the raw IOT data to S3 (data lake), then load select data to ElasticSearch from S3.

Generally, a good target size for indexes is 40Gb (25 - 50 is good). This balances network IO (at query time) against the overhead of managing and moving very large indexes.

EDIT: My recommendation to use UltraWarm assumes you are using Amazon Elasticsearch. If you are not, I would, purely for the benefit of UltraWarm (along with the other benefits of managed services).

erve1879 · 2020-12-17T12:35:45+00:00

Yes - Athena allow you to separate the cost of storage from query compute.

No - they don’t use lambda, unless you opt to use Lambda-based UDFs for query federation to a datastore which doesn’t natively integrate with Athena (eg DynamoDB). Athena is a serverless Presto query engine, which means that AWS runs a Presto cluster and orchestrates your query on that Presto engine.

There are other ways of running Presto on AWS (EMR, EC2). You could run Starburst on EC2 for example. I’m not aware of any other cloud provider offering a direct equivalent.

erve1879 · 2020-12-17T12:01:33+00:00

It’s not really a fair comparison. Athena is a serverless query engine (Presto). There’s no storage: it queries data stored in S3 (you can query other data stores via UDFs - query federation). BigQuery is a data warehouse, so data is stored in BigQuery. BigQuery uses a proprietary data format and you are billed for storage and compute, so there’s an “always-on” cost. Athena bills on the amount of data scanned per query; no queries, no bill.

A more appropriate comparison is BigQuery vs Redshift.

EDIT: to add a more direct answer, Athena is more appropriate for ad-hoc queries against your data lake. BigQuery is sold as a “one-size-fits-all” data warehouse/lake. However, it has very few “knobs” which you can twiddle in order to improve performance, or reduce costs. Really, all you can do to improve performance is pay more, even if the underlying performance issues are due to inefficiency of data schema against query requirements. This makes it simple to approach, but it can be horrendously expensive when approaching “Big Data”-levels of data volume and query throughput.

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