Affinity | A tool to enable affine transformations in javascript

An FTS entity table always has a column named rowid that is the equivalent of an INTEGER PRIMARY KEY index.

In brief SQLite's INTEGER does not imply a Java/Kotlin Integer/Int/int it is a column affinity/type.

If you look at the Datatypes in SQLite3 then an INTEGER can be up to 8 bytes (64 bit signed). Which in Java/Kotlin is long/Long.

Further evidence can be seen with the SQLiteDatabase insert convenience method as it returns the id (rowid) of the inserted row not as an int but as a long.

Returns long - the row ID of the newly inserted row, or -1 if an error occurred.

SQLiteAutoincrement explains about rowid and that in theory it can be from 1-9223372036854775807 (you can even have negative values).

As such, it is wrong to use int/Int/Integer for the rowid as in theory (not that likely in practice), the rowid can be larger than an int/Int/Integer.

I should use INT not LONG.

I'd suggest that you should use Long. It makes no difference to the data as SQLite will store an integer in as little space as it can. Furthermore, it makes no difference to the table(s) that room creates as the COLUMN TYPE will be INTEGER.

I solved this problem by tweaking pod_mutaion_hook code like below.

the issue is that you are using the node affinity while creating the PV.

Which think something like you say inform to Kubernetes my disk will attach to this type of node. Due to affinity your disk or PV is attached to one type of specific node only.

when you are deploying the workload or deployment (POD) it's not getting schedule on that specific node and your POD is not getting that PV or PVC.

to resolve this issue

make sure both POD and PVC schedule at same node add the node affinity to deployment also so POD schedule on that node.

or else

Remove the node affinity rule from PV and create a new PV and PVC and use it.

here is the place where you have mentioned the node affinity rule

If you are using sql light db, then you have to migrate the db in this way,

I haven't tested this but try adding the following statement after line_items = line.split('\t')

Memory consumption of AgglomerativeClustering is O(n²), it means it grows exponentially compared to data size. With single linkage, the computation can be made faster from O(n³) to O(n²) but unfortunately this does not apply to memory [1]. Single clustering also has down sides of "rich get richer" kind of behavior where the clusters tend to have only a few big ones and others near to zero size clusters [2]. So, at least inside scipy or scikit options on fine tuning are not good.

Another option would be have less input data when fitting the model (= making the training). For that you could use for data frame a method (assuming the data object is a dataframe):

For the ELK stack to work you need all three PersistentVolumeClaim's to be bound as I recall. Instead of creating 1 30 GB of PV create 3 of the same size with the claims and then re-install. Other nodes have unmet dependincies.

Also please do not handle the volumes by hand. There are guidelines to deploy dynamic volums. Use OpenEBS for example. That way you wont need to worry about the pvc's. After giving the pv's if anything happens write again with your cluster installation process.

I was wrong obviously, in this particular problem, filesystems and cgroups take role and the main problem of this is an old problem. From 5.2.1 to 8.0.0. Reinstall the chart by pulling the chart. Edit values file and definitely change the container version. It should be fine or create another error log stack.

the way you have passed value in prometheus.yaml is wrong

There were two issues with the deployment setup:

The nodeSelector specified in the Deployment manifest was using wrong label