Bucket | Supports | Caching library
kandi X-RAY | Bucket Summary
kandi X-RAY | Bucket Summary
Bucket is a disk cache library for Android. You can use it to cache any object that can be serialized to json. DiskLruCache by Jake Wharton is used as the underlying cache. Bucket contains synchronous, async and Rx methods for all operations.
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Top functions reviewed by kandi - BETA
- Puts an object into cache
- Put object to cache
- Put object in cache
- Opens a stream for the given key
- Region async methods
- Get from cache
- Creates an Observable for the given Callable
- Returns the internal key for the given key
- Searches for a key in cache asynchronously
- Check if the cache contains the given key
- Checks whether the given key exists in the cache
- Returns true if the cache contains a value
- Removes a value from the cache
- Remove cache value
- Removes the specified value from the cache
- Removes the given key from the cache
- Clears all cache values
- Clears all cached values
- Clears the cache
- Writes a single byte
- Writes the buffer to the output stream
- Writes the buffer
- Create a builder
- Deletes the cache directory
- Flushes the stream
- Close the editor
Bucket Key Features
Bucket Examples and Code Snippets
Lines of Code : 87License : Permissive (MIT)
Copycompile 'com.github.simonpercic:bucket:1.0.0'
// create a singleton instance using a builder()
int maxSizeBytes = 1024 * 1024;
Bucket bucket = Bucket.builder(context, maxSizeBytes).build();
// create a singleton instance using a builder()
Gson gson 
License : No LicenseCopyconst bucketSort = (arr, size = 5) => {
const min = Math.min(...arr);
const max = Math.max(...arr);
const buckets = Array.from(
{ length: Math.floor((max - min) / size) + 1 },
() => []
);
arr.forEach(val => {
buckets[Ma // generics
public class GenericObject {
T object;
String value;
}
Type genericType = new TypeToken>(){}.getType();
GenericObject object = bucket.get("key", genericType);
// collections
Type collectionType = new TypeToken>() {}.getTy def categorical_column_with_hash_bucket(key,
hash_bucket_size,
dtype=dtypes.string):
"""Represents sparse feature where ids are set by hashing.
Use this when your sp def _categorical_column_with_hash_bucket(key,
hash_bucket_size,
dtype=dtypes.string):
"""Represents sparse feature where ids are set by hashing.
Use this when your def sequence_categorical_column_with_hash_bucket(
key, hash_bucket_size, dtype=dtypes.string):
"""A sequence of categorical terms where ids are set by hashing.
Pass this to `embedding_column` or `indicator_column` to convert sequence
categ Community Discussions
Trending Discussions on Bucket
QUESTION
Background
I have a complex nested JSON object, which I am trying to unpack into a pandas df in a very specific way.
JSON Object
this is an extract, containing randomized data of the JSON object, which shows examples of the hierarchy (inc. children) for 1x family (i.e. 'Falconer Family'), however there is 100s of them in total and this extract just has 1x family, however the full JSON object has multiple -
ANSWER
Answered 2022-Feb-16 at 06:41I think this gets you pretty close; might just need to adjust the various name columns and drop the extra data (I kept the grouping column).
The main idea is to recursively use pd.json_normalize with pd.concat for all availalable children levels.
EDIT: Put everything into a single function and added section to collapse the name columns like the expected output.
QUESTION
Just today, whenever I run terraform apply, I see an error something like this: Can't configure a value for "lifecycle_rule": its value will be decided automatically based on the result of applying this configuration.
It was working yesterday.
Following is the command I run: terraform init && terraform apply
Following is the list of initialized provider plugins:
...ANSWER
Answered 2022-Feb-15 at 13:49Terraform AWS Provider is upgraded to version 4.0.0 which is published on 10 February 2022.
Major changes in the release include:
- Version 4.0.0 of the AWS Provider introduces significant changes to the aws_s3_bucket resource.
- Version 4.0.0 of the AWS Provider will be the last major version to support EC2-Classic resources as AWS plans to fully retire EC2-Classic Networking. See the AWS News Blog for additional details.
- Version 4.0.0 and 4.x.x versions of the AWS Provider will be the last versions compatible with Terraform 0.12-0.15.
The reason for this change by Terraform is as follows: To help distribute the management of S3 bucket settings via independent resources, various arguments and attributes in the aws_s3_bucket resource have become read-only. Configurations dependent on these arguments should be updated to use the corresponding aws_s3_bucket_* resource. Once updated, new aws_s3_bucket_* resources should be imported into Terraform state.
So, I updated my code accordingly by following the guide here: Terraform AWS Provider Version 4 Upgrade Guide | S3 Bucket Refactor
The new working code looks like this:
QUESTION
I am following this tutorial on migrating data from an oracle database to a Cloud SQL PostreSQL instance.
I am using the Google Provided Streaming Template Datastream to PostgreSQL
At a high level this is what is expected:
- Datastream exports in Avro format backfill and changed data into the specified Cloud Bucket location from the source Oracle database
- This triggers the Dataflow job to pickup the Avro files from this cloud storage location and insert into PostgreSQL instance.
When the Avro files are uploaded into the Cloud Storage location, the job is indeed triggered but when I check the target PostgreSQL database the required data has not been populated.
When I check the job logs and worker logs, there are no error logs. When the job is triggered these are the logs that logged:
...ANSWER
Answered 2022-Jan-26 at 19:14This answer is accurate as of 19th January 2022.
Upon manual debug of this dataflow, I found that the issue is due to the dataflow job is looking for a schema with the exact same name as the value passed for the parameter databaseName and there was no other input parameter for the job using which we could pass a schema name. Therefore for this job to work, the tables will have to be created/imported into a schema with the same name as the database.
However, as @Iñigo González said this dataflow is currently in Beta and seems to have some bugs as I ran into another issue as soon as this was resolved which required me having to change the source code of the dataflow template job itself and build a custom docker image for it.
QUESTION
I have this Js function with hard coded filter parameters. It filter all the buckets sub objects when key start with a string from a given list. For now i havent found a way to put this list as an array...
...ANSWER
Answered 2022-Jan-25 at 16:55Use array.every() to check all the elements of the array.
QUESTION
I would like to send arguments when I call an anchor with bitbucket pipelines
Here is the file I am using, I have to call after-script because I need to push to a certain S3 bucket
ANSWER
Answered 2022-Jan-21 at 19:45To the best of my knowledge, you can only override particular values of YAML anchors. Attempts to 'pass arguments' won't work.
Instead, Bitbucket Pipelines provide Deployments - an ad-hoc way to assign different values to your variables depending on the environment. You'll need to create two deployments (say, dev and uat), and use them when referring to a step:
QUESTION
I am trying to set up Firebase with next.js. I am getting this error in the console.
FirebaseError: Expected first argument to collection() to be a CollectionReference, a DocumentReference or FirebaseFirestore
This is one of my custom hook
...ANSWER
Answered 2022-Jan-07 at 19:07Using getFirestore from lite library will not work with onSnapshot. You are importing getFirestore from lite version:
QUESTION
(Solution has been found, please avoid reading on.)
I am creating a pixel art editor for Android, and as for all pixel art editors, a paint bucket (fill tool) is a must need.
To do this, I did some research on flood fill algorithms online.
I stumbled across the following video which explained how to implement an iterative flood fill algorithm in your code. The code used in the video was JavaScript, but I was easily able to convert the code from the video to Kotlin:
https://www.youtube.com/watch?v=5Bochyn8MMI&t=72s&ab_channel=crayoncode
Here is an excerpt of the JavaScript code from the video:
Converted code:
...ANSWER
Answered 2021-Dec-29 at 08:28I think the performance issue is because of expandToNeighbors method generates 4 points all the time. It becomes crucial on the border, where you'd better generate 3 (or even 2 on corner) points, so extra point is current position again. So first border point doubles following points count, second one doubles it again (now it's x4) and so on.
If I'm right, you saw not the slow method work, but it was called too often.
QUESTION
I'm trying to communicate with an OPC DA server and need to write in a tag which is in an array format. We can connect with a simulation server, read tags (int, real, array) and write tags (int, real, str). The problem comes when we need to write in an array tag. The developper of the OpenOPC library (Barry Barnreiter) recommand to use a VARIANT variable because OPC "expect to see a Windows VARIANT structure when writing complex objects such as arrays".
- I did install Pywin32 (build 217) as suggested here.
- I tried to send a simple integer instead of an array in a VARIANT structure.
Here's the code:
...ANSWER
Answered 2021-Dec-05 at 19:56You need to upgrade the python to 3.9 and Pywin32 to Build 302. In addition, you need to install the OpenOPC-Python3x 1.3.1.
QUESTION
When I try to read my Firebase Storage data I'm getting the following error:
...ANSWER
Answered 2021-Nov-19 at 20:33Following the steps in this post fixed my issue:
This is due to a missing permission:
QUESTION
In short:
I have implemented a simple (multi-key) hash table with buckets (containing several elements) that exactly fit a cacheline. Inserting into a cacheline bucket is very simple, and the critical part of the main loop.
I have implemented three versions that produce the same outcome and should behave the same.
The mystery
However, I'm seeing wild performance differences by a surprisingly large factor 3, despite all versions having the exact same cacheline access pattern and resulting in identical hash table data.
The best implementation insert_ok suffers around a factor 3 slow down compared to insert_bad & insert_alt on my CPU (i7-7700HQ).
One variant insert_bad is a simple modification of insert_ok that adds an extra unnecessary linear search within the cacheline to find the position to write to (which it already knows) and does not suffer this x3 slow down.
The exact same executable shows insert_ok a factor 1.6 faster compared to insert_bad & insert_alt on other CPUs (AMD 5950X (Zen 3), Intel i7-11800H (Tiger Lake)).
ANSWER
Answered 2021-Oct-25 at 22:53The TLDR is that loads which miss all levels of the TLB (and so require a page walk) and which are separated by address unknown stores can't execute in parallel, i.e., the loads are serialized and the memory level parallelism (MLP) factor is capped at 1. Effectively, the stores fence the loads, much as lfence would.
The slow version of your insert function results in this scenario, while the other two don't (the store address is known). For large region sizes the memory access pattern dominates, and the performance is almost directly related to the MLP: the fast versions can overlap load misses and get an MLP of about 3, resulting in a 3x speedup (and the narrower reproduction case we discuss below can show more than a 10x difference on Skylake).
The underlying reason seems to be that the Skylake processor tries to maintain page-table coherence, which is not required by the specification but can work around bugs in software.
The DetailsFor those who are interested, we'll dig into the details of what's going on.
I could reproduce the problem immediately on my Skylake i7-6700HQ machine, and by stripping out extraneous parts we can reduce the original hash insert benchmark to this simple loop, which exhibits the same issue:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Bucket
You can use Bucket like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Bucket component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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