For most use-cases (indexing denormalised data as documents then running searches against them), there's little difference between the two. The mechanics of how the cluster operates are almost identical.
There are some Elasticsearch features that were part of X-Pack (their commercial offering) so aren't included in the OpenSearch fork. Some of those features are really nice to have and make life much easier; the enrich ingest processor is something I really miss in OpenSearch.
The biggest differences are in the tooling around Elasticsearch. All the observability stuff, the SIEM features, various integrations, and now the AI fluff. I've worked with clients in different sectors and - aside from the observability stuff (which is really nice) - none have had an appetite for any of that.
The OpenSearch team is doing some really cool stuff and the project has come a long way. I'm sure it'll continue to improve. It has a very loyal customer base and even has its own annual event; OpenSearchCon 2024 is next month!
My Mum went through CAR-T for lymphoma earlier this year. It's a brutal therapy but can offer benefits in the long term.
As you mentioned, the big issues are around infections. It completely wipes out the immune system, including all vaccinations. Every vaccination needs to be taken again, once the body is recovered from the initial therapy.
My Mum recently contracted COVID and is in hospital being given Paxlovid. She had COVID a while ago and it was nothing compared to her current state. CAR-T made it significantly worse but will hopefully be worth it in the long term.
I'm saddened by your news but - given what I've experienced with my Mum during her cancer journey - can understand the difficult decision you've made.
I would advise taking a K2 MK7 supplement with D3, or get a supplement that contains both. Personally, I take one capsule a day that contains both 3000IU of D3 and 100μg of K2 MK7.
D3 by itself can cause high calcium levels, to the point of toxicity in very high doses. The K2 moves the calcium from the blood to bones.
Insulin levels are massively overlooked. You can have normal blood glucose but excessive insulin levels due to insulin resistance, which leads to type 2 diabetes.
As another poster mentioned, it won't happen either invasively or non-invasively any time soon. Insulin levels aren't even part of a standard blood test in most places due to the cost.
Now imagine having that entire responsibility yourself, having to do it all manually. That's what we type 1 diabetics do every day!
The nighttime fluctuations could be legit changes in glucose level, but could also be weird sensor issues. The sensor will often give low readings when lying on it in your sleep. These are referred to as 'compression lows'.
The morning glucose spike you noticed is called the 'dawn phenomenon'. I inject a little insulin every morning to counteract it.
It's difficult to tell if IF alone had a change because I also switched to a very low carbohydrate diet at pretty-much the same time. The two combined had a massively positive effect on my diabetes.
My glucose level is much more stable, and A1C is lower. I also use significantly less insulin than a lot of people with type 1 diabetes.
My reason for doing both was to reduce insulin levels as much as possible.
The problem with looking at what something delivers is that the time between taking the action and noticing what was delivered. There's often days, weeks, or even months (especially in the case of going to the gym) between doing something and seeing a difference.
When taking a new action (or new inaction), you can even feel worse before feeling better.
It's a good idea but the temporal component skews everything.
Habit formation, even if it's a new habit of not doing something, is something that's worked for me in the past.
Type 1 diabetic here. My body doesn't produce insulin, so if my glucose level goes up after eating or drinking something, that increase in glucose is a (very) approximate measure of insulin response in someone without type 1 diabetes. Insulin is released/produced/injected in response to glucose level increase (and other signals in someone who doesn't have type 1 diabetes).
In my experience, black coffee is absolutely fine outside of an eating window. I fast for at least 18 hours a day, only drinking black coffee and water when fasting. There was no measurable difference in glucose on occasions when I drank black coffee, vs occasions without (all other things as equal as possible).
No mention of Grenadier apples, unfortunately. Perhaps understandably so.
They're impossible to find in most shops due to them not storing well and being quite ugly. We have a tree in our garden and the apples are outstanding for pies, crumbles, cobblers, and Charlottes. They can hold their shape well, or turn to a delicious purée if cooked longer.
I've been using Dark Sky for years but I uninstalled it yesterday. Since the acquisition, the native Weather app has been massively upgraded with a lot of Dark Sky's best features and is very good now.
I ran the two in parallel for a few months to compare the alerts and accuracy. Weather won out.
Elasticsearch is fine for time series data. A lot of tasks are actually easier with time series data. You add a field called `@timestamp` to your documents and a lot of analysis becomes possible, like date histograms, date range queries, ML jobs, etc.
Immutable time series data like logs and metrics are a great fit for Elasticsearch due to the way Lucene stores data. Documents in Lucene are immutable so an update in Elasticsearch is creates a new document and places a tombstone marker on the old one. Immutable data means you don't have to tolerate those inefficiencies.
Dashboards don't load the entire dataset by default. I can't remember what the exact default time range is but I think it's ~15 minutes or so. They're fairly quick to render in Kibana.
Elasticsearch is a great tool for observability data (logs, metrics, and APM data). Elastic's tooling makes a lot of this really easy in most cases.
> I have little knowledge of the log aggregation domain, but generally indices are great for read mostly loads.
Generally, you write and read to/from the same index in Elasticsearch. Where this falls apart is that you'll often want to change the configuration for an index based on whether it's write or read heavy. The main thing that changes in this scenario is the number of primary and replica shards (Lucene indices) for the Elasticsearch index.
Indices with a high write, low search workload will generally require more primary shards and less replicas. Low write, high search workloads require the opposite; lower primaries and more replicas.
The problem comes when you need high write and high search rates. Using a single cluster with lots of primaries and lots of replicas will overwhelm the hosts and you end up with terrible performance. The general pattern with Elasticsearch is to run two clusters. Index into one cluster, then use cross-cluster-replication (CCR) into a different cluster you run queries against.
There's an incredible amount of nuance to all of this. I've worked with many clusters and they all have different usage and configuration requirements. There's no magic formula for calculating configuration values; it all comes down to experience, monitoring, and experimentation.
I believe the complaints here are a case of 'not using it correctly'.
The 'Reverse index' (Lucene's inverted index) is a fundamental data structure used to enable very fast search. Other data structures, like KD trees, are used for non-text data types. If you're not doing full text search, don't use `text` fields. If you're not querying the data, why store it in the first place?
Full text search for logs is incredibly useful for log files when combined with alerting. If you get log entries indicating that a disk is full, a service has stopped, or a user account is blocked, Elasticsearch can (with the right license) send emails or post on Slack.
Static mappings can be a pain but if you're constantly increasing the maximum field count for an index, use different indices for different log sources. Come up with an index pattern or alias that allows querying all those indices at the same time.
The main task here is reconciling the different logs so the index mappings are easily searchable, effectively as a union. Elastic Common Schema helps a lot with this. Elasticsearch mappings are easier to build when you first consider the queries you're going to be running on the data. You can then design the mapping with the right structure, field types, and settings.
> To reiterate, I take zero boluses when I’m on the keto diet.
I'm very surprised by this and would love to hear what you eat while doing keto. I'm T1 but still have to bolus when doing keto, even when doing zero carb and fasting for 18+ hours a day. I only need between 1 and 3 units but there's rarely an occasion when I don't need to bolus at all.
> Doctors are against it because they’re dealing with ketoacidosis on a regular basis.
The issue my endo has is that my BG is much lower than he would like. I've heard the phrase "too well controlled" so many times and it irritates me no end. His concern is that I'll lose awareness of low BG, and can't comprehend how I'm able to function as usual even when my BG is ~2.5 mmol/l (to clarify - I don't aim to run that low; my target range is 4-6).
A large (135cm diameter) umbrella. It cost just under £30 and I love using it.
I've always had a good outdoor coat but my legs and feet would still get wet. With the umbrella, only my toes get wet in a downpour. It's large enough for my two kids and I to get under!
I work remotely and rent an office in town. It's a 15 minute walk from my house, a decent size (~22m^2), all my own, and well set up. I love it and pay gladly but I it's not possible for everyone.
I used to have a garden office but my wife now needs her own space for her work so I took the rented space.
A dedicated space is a must if you're working remotely. Either at home or offsite.
I have no objection to going into the office for well defined, productive meetings like project kick-offs though. I still believe they're the best way to collaborate on big planning tasks, even after having worked remotely for over 10 years.
There are some Elasticsearch features that were part of X-Pack (their commercial offering) so aren't included in the OpenSearch fork. Some of those features are really nice to have and make life much easier; the enrich ingest processor is something I really miss in OpenSearch.
The biggest differences are in the tooling around Elasticsearch. All the observability stuff, the SIEM features, various integrations, and now the AI fluff. I've worked with clients in different sectors and - aside from the observability stuff (which is really nice) - none have had an appetite for any of that.
The OpenSearch team is doing some really cool stuff and the project has come a long way. I'm sure it'll continue to improve. It has a very loyal customer base and even has its own annual event; OpenSearchCon 2024 is next month!