htable-java | Hash array mapped trie for java | Hashing library

 by   anti-social Java Version: Current License: No License

kandi X-RAY | htable-java Summary

kandi X-RAY | htable-java Summary

htable-java is a Java library typically used in Security, Hashing, Example Codes applications. htable-java has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Hash array mapped trie for java

            kandi-support Support

              htable-java has a low active ecosystem.
              It has 3 star(s) with 1 fork(s). There are 1 watchers for this library.
              It had no major release in the last 6 months.
              htable-java has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of htable-java is current.

            kandi-Quality Quality

              htable-java has no bugs reported.

            kandi-Security Security

              htable-java has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              htable-java does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              htable-java releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed htable-java and discovered the below as its top functions. This is intended to give you an instant insight into htable-java implemented functionality, and help decide if they suit your requirements.
            • Computes the minimum number of bytes required to hold a given value
            • Convert a byte array to a double
            • To long
            • Convert a float to a byte array
            • Convert an integer to a byte array
            • Converts a double to a byte array
            • To byte array
            • Converts a byte array to a float
            • To int
            • Computes the minimum number of bytes required to hold the given value
            • Convert a collection of bytes to a byte array
            • To short array
            • Convert a collection of long values to a long array
            • Convert a collection of Integer to an integer array
            • Convert a collection of float values to an array of float values
            • Converts a collection of Double into an array of double values
            • Convert a collection of Byte objects to a byte array
            Get all kandi verified functions for this library.

            htable-java Key Features

            No Key Features are available at this moment for htable-java.

            htable-java Examples and Code Snippets

            No Code Snippets are available at this moment for htable-java.

            Community Discussions


            Find near duplicate and faked images
            Asked 2022-Mar-24 at 01:32

            I am using Perceptual hashing technique to find near-duplicate and exact-duplicate images. The code is working perfectly for finding exact-duplicate images. However, finding near-duplicate and slightly modified images seems to be difficult. As the difference score between their hashing is generally similar to the hashing difference of completely different random images.

            To tackle this, I tried to reduce the pixelation of the near-duplicate images to 50x50 pixel and make them black/white, but I still don't have what I need (small difference score).

            This is a sample of a near duplicate image pair:

            Image 1 (a1.jpg):

            Image 2 (b1.jpg):

            The difference between the hashing score of these images is : 24

            When pixeld (50x50 pixels), they look like this:



            The hashing difference score of the pixeled images is even bigger! : 26

            Below two more examples of near duplicate image pairs as requested by @ann zen:

            Pair 1

            Pair 2

            The code I use to reduce the image size is this :



            Answered 2022-Mar-22 at 12:48

            Rather than using pixelisation to process the images before finding the difference/similarity between them, simply give them some blur using the cv2.GaussianBlur() method, and then use the cv2.matchTemplate() method to find the similarity between them:



            Is there a need for transitivity in Python __eq__?
            Asked 2022-Mar-15 at 07:46

            I'm implementing my own class, with custom __eq__. And I'd like to return True for things that are not "equal" in a mathematical sense, but "match" in a fuzzy way.

            An issue with this is, however, that this leads to loss of transitivity in a mathematical sense, i.e. a == b && b ==c, while a may not be equal to c.

            Question: is Python dependent on __eq__ being transitive? Will what I'm trying to do break things, or is it possible to do this as long as I'm careful myself not to assume transitivity?

            Use case

            I want to match telephone numbers with one another, while those may be either formatted internationally, or just for domestic use (without a country code specified). If there's no country code specified, I'd like a number to be equal to a number with one, but if it is specified, it should only be equal to numbers with the same country-code, or without one.


            • Of course, +31 6 12345678 should equal +31 6 12345678, and 06 12345678 should equal 06 12345678
            • +31 6 12345678 should equal 06 12345678 (and v.v.)
            • +49 6 12345678 should equal 06 12345678 (and v.v.)
            • But +31 6 12345678 should not be equal to +49 6 12345678

            Edit: I don't have a need for hashing (and so won't implement it), so that at least makes life easier.



            Answered 2022-Mar-14 at 18:06

            There is no MUST but a SHOULD relation for comparisons being consistent with the commonly understood relations. Python expressively does not enforce this and float is an inbuilt type with different behaviour due to float("nan").

            Expressions: Value comparisons

            User-defined classes that customize their comparison behavior should follow some consistency rules, if possible:

            • […]
            • Comparison should be symmetric. In other words, the following expressions should have the same result:
              • x == y and y == x
              • x != y and y != x
              • x < y and y > x
              • x <= y and y >= x
            • Comparison should be transitive. The following (non-exhaustive) examples illustrate that:
              • x > y and y > z implies x > z
              • x < y and y <= z implies x < z

            Python does not enforce these consistency rules. In fact, the not-a-number values are an example for not following these rules.

            Still, keep in mind that exceptions are incredibly rare and subject to being ignored: most people would treat float as having total order, for example. Using uncommon comparison relations can seriously increase maintenance effort.

            Canonical ways to model "fuzzy matching" via operators are as subset, subsequence or containment using unsymmetric operators.

            • The set and frozenset support >, >= and so on to indicate that one set encompases all values of another.



            Unhashing a hashed (MD5) email address
            Asked 2022-Feb-15 at 15:55

            I know that in hashing you, by definition, lose information. However, as email addresses can be restricted - such as with the information available I would know a potential domain of the email, and that it must have an @. Do these constraints change anything about the problem? Or is the best way to simply make a guess and see if the hash is the same? Also MD5 is no longer as secure as it once was.




            Answered 2022-Feb-15 at 15:55

            That is the point of Md5 hashing that even a minute change in the string can change the hash completely. So these constraints change nothing about the problem.

            However since you said that its an email and that you know about the potential domain then you can try this technique.

            1. Generate a list of potential emails it will be within 26 letters and lets say of maximum size 10.

            Then you can generate an md5 for all of these possibilities and check if it is equal to the one you have.



            Channel hangs, probably not closing at the right place
            Asked 2022-Jan-29 at 19:46

            I'm trying to learn Go while writing a small program. The program should parse a PATH recursivelys as efficient and fast as possible and output the full filename (with the path included) and the sha256 file hash of the file.

            If the file hashing generates fails, I wanna keep the error and add it to the string (at the hash position).

            The result should return a string on the console like: fileXYZ||hash

            Unfortunately, the programs hangs at some point. I guess some of my channels are not closing properly and waiting indefinitely for input. I've been trying for quite some time to fix the problem, but without success.

            Does anyone have an idea why the output hangs? Many many thx in advance, any input/advice for a Go newcomer is welcome too ;-).

            (I wrote separate functions as I wanna add additional features after having fixed this issue.)

            Thanks a lot! Didier

            Here is the code:



            Answered 2022-Jan-29 at 19:46

            The following loop hangs because chashes is not closed.



            How can I join two lists in less than O(N*M)?
            Asked 2021-Dec-25 at 00:43

            Assume we have two tables (think as in SQL tables), where the primary key in one of them is the foreign key in the other. I'm supposed to write a simple algorithm that would imitate the joining of these two tables. I thought about iterating over each element in the primary key column in the first table, having a second loop where it checks if the foreign key matches, then store it in an external array or list. However, this would take O(N*M) and I need to find something better. There is a hint in the textbook that it involves hashing, however, I'm not sure how hashing could be implemented here or how it would make it better?

            Editing to add an example:



            Answered 2021-Dec-24 at 22:18

            Read the child table's primary and foreign keys into a map where the keys are the foreign keys and the values are the primary keys. Keep in mind that one foreign key can map to multiple primary keys if this is a one to many relationship.

            Now iterate over the primary keys of the mother table and for each primary key check whether it exists in the map. If so, you add a tuple of the primary keys of the rows that have a relation to the array (or however you want to save it).

            The time complexity is O(n + m). Iterate over the rows of each table once. Since the lookup in the map is constant, we don't need to add it.

            Space complexity is O(m) where m is the number of rows in the child table. This is some additional space you use in comparison to the naive solution to improve the time complexity.



            How reproducible / deterministic is Parquet format?
            Asked 2021-Dec-09 at 03:55

            I'm seeking advice from people deeply familiar with the binary layout of Apache Parquet:

            Having a data transformation F(a) = b where F is fully deterministic, and same exact versions of the entire software stack (framework, arrow & parquet libraries) are used - how likely am I to get an identical binary representation of dataframe b on different hosts every time b is saved into Parquet?

            In other words how reproducible Parquet is on binary level? When data is logically the same what can cause binary differences?

            • Can there be some uninit memory in between values due to alignment?
            • Assuming all serialization settings (compression, chunking, use of dictionaries etc.) are the same, can result still drift?

            I'm working on a system for fully reproducible and deterministic data processing and computing dataset hashes to assert these guarantees.

            My key goal has been to ensure that dataset b contains an idendital set of records as dataset b' - this is of course very different from hashing a binary representation of Arrow/Parquet. Not wanting to deal with the reproducibility of storage formats I've been computing logical data hashes in memory. This is slow but flexible, e.g. my hash stays the same even if records are re-ordered (which I consider an equivalent dataset).

            But when thinking about integrating with IPFS and other content-addressable storages that rely on hashes of files - it would simplify the design a lot to have just one hash (physical) instead of two (logical + physical), but this means I have to guarantee that Parquet files are reproducible.


            I decided to continue using logical hashing for now.

            I've created a new Rust crate arrow-digest that implements the stable hashing for Arrow arrays and record batches and tries hard to hide the encoding-related differences. The crate's README describes the hashing algorithm if someone finds it useful and wants to implement it in another language.

            I'll continue to expand the set of supported types as I'm integrating it into the decentralized data processing tool I'm working on.

            In the long term, I'm not sure logical hashing is the best way forward - a subset of Parquet that makes some efficiency sacrifices just to make file layout deterministic might be a better choice for content-addressability.



            Answered 2021-Dec-05 at 04:30

            At least in arrow's implementation I would expect, but haven't verified the exact same input (including identical metadata) in the same order to yield deterministic outputs (we try not to leave uninitialized values for security reasons) with the same configuration (assuming the compression algorithm chosen also makes the deterministic guarantee). It is possible there is some hash-map iteration for metadata or elsewhere that might also break this assumption.

            As @Pace pointed out I would not rely on this and recommend against relying on it). There is nothing in the spec that guarantees this and since the writer version is persisted when writing a file you are guaranteed a breakage if you ever decided to upgrade. Things will also break if additional metadata is added or removed ( I believe in the past there have been some big fixes for round tripping data sets that would have caused non-determinism).

            So in summary this might or might not work today but even if it does I would expect this would be very brittle.



            Angular 12 app still being cached with output-hashing=all
            Asked 2021-Dec-03 at 14:26

            I have an Angular 12 application that has different build environments (dev/staging/prod) and I have configured these with output hashing on in angular.json:



            Answered 2021-Nov-25 at 08:51

            In case you're using a service worker (eg @angular/pwa which installs @angular/service-worker along), you're entire angular app is being cached by the browser. This includes index.html + all javascript files + all stylesheets.

            To have a new version of your application pushed to your users, you have to do 2 things:

            Update your ngsw-config.json on each new release:



            Where to store access token and how to keep track of user (using JWT token in Http only cookie)
            Asked 2021-Nov-16 at 08:54

            Trying to understand how to get and then save user in client (using JWT token in Http only cookie), so that I can do conditional rendering. What I'm having difficulty with is how to continously know if the user is logged in or not, without having to send a request to the server each time the user changes/refresh page. (Note: the problem is not how do I get the token in the Http only cookie, I know that this is done through withCredentials: true)

            So my problem is how do you get/store the access token so that the client will not have to make a request to the server each time the user does something on the website. For example the Navbar should do conditional renderingen depending on if the user is logged in or not, then I don't want to do "ask the server if the user has a access token, then if not check if user has refresh token, then return a new access token if true else redirect to login page" every single time the user switches page.





            Answered 2021-Nov-16 at 08:54

            Do I really need to do a request to the server each time the user switches page or refresh page?

            That is the safest way. If you want to keep with the current security best practices for SPAs, then using http-only, secure, same-site cookies is the best option. Refreshes won't happen that often on your page, so it shouldn't be a problem.

            My initial idea was to use useEffect in the App component where I make a call to the function GetUser() which makes a request to "/get-user" which will user the refreshToken to find the user

            What I would do is to first verify the access token, if it's valid then take the userId out of the access token (if you don't have it there you can easily add it as you're creating the tokens manually) and read the user data from the database. If the access token is invalid then return an error to the website and let the user use the refresh token to get a new access token. So I wouldn't mix responsibilities here - I wouldn't use refresh token to get information about the logged in user.

            Also I have a question about when I should be calling "/token" in the server to create new access tokens. Should I always try to use the access token to do things that require authentication and if it for example returns null at some point then I make request to "/token" and after that repeat what the user was trying to do?

            Yes, that's how it usually is implemented. You make a call with the access token to a protected endpoint. It would be best if the endpoint returned 401 response if the token is expired or invalid. Then your app knows that it should use the refresh token to get a new access token. Once you have a new access token you try to make the call to the protected endpoint again. If you don't manage to get a new access token (e.g. because the refresh token has expired), then you ask the user to log in again.



            Flutter Web Page Routing Issue
            Asked 2021-Oct-22 at 07:31

            I need web app with base url as



            Answered 2021-Oct-22 at 07:31

            I'd advice you commenting out href in 'web/index.html' (platform project automatically generated when adding Web). That's how I did it:

            And here's the example of this app working under virtual directory:

            Flutter Web somehow has these silly issues in scaffolding for the web project (href in index.html, wrong paths for service worker etc.) - discovered this while playing with GitHub pages.



            Ionic + Fastlane | Android "error: package does not exist"
            Asked 2021-Sep-19 at 15:32

            I have an Ionic project I'm working with that is having trouble building to Android. I inherited this project, so that's why I'm not 100% familiar with Fastlane and how it's building the java files. Additionally, I'm on WSL2 and using sdkmanager with the following installed packages:



            Answered 2021-Sep-19 at 15:32

            cordova-plugin-androidx-adapter will migrate older libraries to use AndroidX Support Libraries automatically. I believe this is needed when you target Android 10 or higher, which is when the switch was made. Once all of your plugins support AndroidX, you can remove the adapter plugin.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


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            Install htable-java

            You can download it from GitHub.
            You can use htable-java 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 htable-java 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 For Gradle installation, please refer .


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