chunker | Implementation of Content Defined Chunking in Go
kandi X-RAY | chunker Summary
kandi X-RAY | chunker Summary
The package chunker implements content-defined-chunking (CDC) based on a rolling Rabin Hash. The library is part of the restic backup program. An introduction to Content Defined Chunking can be found in the restic blog post Foundation - Introducing Content Defined Chunking (CDC). You can find the API documentation at
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QUESTION
I used an NLP chunker that splits incorrectly the term 'C++' and 'C#' as: C (NN), +(SYM), +(SYM), C (NN), #(SYM).
The resulting list of incorrect chunking looks like this:
...ANSWER
Answered 2022-Jan-07 at 08:42Basically, I just appended each letter one by one.
When there's a match with the two strings we're looking for ("C++" or "C#"), it will add that value to the list and reset the string.
QUESTION
I'm currently trying to write and integration flow then reads a csv file and processes it in chunks (Calls API for enrichment) then writes in back out as a new csv. I currently have an example working perfectly except that it is polling a directory. What I would like to do is be able to pass the file-path and file-name to the integration flow in the headers and then just perform the operation on that one file.
Here is my code for the polling example that works great except for the polling.
...ANSWER
Answered 2021-Oct-19 at 19:38If you know the file, then there is no reason in any special component from the framework. You just start your flow from a channel and send a message to it with File object as a payload. That message is going to be carried on to the slitter in your flow and everything is going to work OK.
If you really want to have a high-level API on the matter, you can expose a @MessagingGateway as a beginning of that flow and end-user is going to call your gateway method with desired file as an argument. The framework will create a message on your behalf and send it to the message channel in the flow for processing.
See more info in docs about gateways:
And also a DSL definition starting from some explicit channel:
https://docs.spring.io/spring-integration/docs/current/reference/html/dsl.html#java-dsl-channels
QUESTION
I am using SQL Alchemy engine along with pandas and trying to implement fast_executemany=True but I am getting this error while I tried to insert df frames rows to a SQL SERVER DB.
My code is something like this
...ANSWER
Answered 2021-Oct-18 at 19:07Gord was right, there were numeric columns created as varchar(max). I had to cast them manually while creating dataframe
QUESTION
I'm trying to Stream JSON from MongoDB to S3 with the new version of @aws-sdk/lib-storage:
...ANSWER
Answered 2021-Oct-07 at 15:58After reviewing your error stack traces, probably the problem has to do with the fact that the MongoDB driver provides a cursor in object mode whereas the Body parameter of Upload requires a traditional stream, suitable for be processed by Buffer in this case.
Taking your original code as reference, you can try providing a Transform stream for dealing with both requirements.
Please, consider for instance the following code:
QUESTION
I'm trying to implement a simple HEX Viewer by using three Text() boxes which are set to scroll simultaneously.
However, it seems as though there's some kind of "drift" and at some point, the first box loses alignment with the other two. I can't seem to figure out why.
...ANSWER
Answered 2021-Oct-01 at 15:46Inside _populate_address_area, there is a for loop: for i in range(num_lines + 1):. This is the cause of the problem. Using num_lines + 1 adds one too many linse to textbox_address. To fix this, there are two options: deleting the + 1, or using for i in range(1, num_lines + 1):. Either way, textbox_address will have the correct number of lines.
QUESTION
So I made a program that calculates primes to test what the difference is between using multithreading or just using single thread. I read that multiprocessing bypasses the GIL, so I expected a decent performance boost.
So here we have my code to test it:
...ANSWER
Answered 2021-Mar-23 at 20:42from multiprocessing.dummy import Pool
from time import time as t
pool = Pool(12)
QUESTION
While using pyspark and nltk, I want to get the length of all "NP" words and sort them in decending order. I am currently stuck on the navigation of the subtree.
example subtree output.
...ANSWER
Answered 2021-Mar-15 at 06:56You can add a type check for each entry to prevent errors:
QUESTION
I have to analyse a large text dataset using Spacy. the dataset contains about 120000 records with a typical text length of about 1000 words. Lemmatizing the text takes quite some time so I looked for methods to reduce that time. This arcicle describes how to speed up the computations using joblib. That works reasonably well: 16 cores reduce the CPU time with a factor of 10, the hyperthreads reduce it with an extra 7%.
Recently I realized that I wanted to compute similarities between docs and probably more analyses with docs later on. So I decided to generate a Spacy document instance () for all documents and use that for analyses (lemmatizing, vectorizing, and probably more) later on. This is where the trouble started.
The analyses of the parallel lemmatizer take place in the function below:
...ANSWER
Answered 2021-Feb-23 at 12:00A pickled doc is quite large and contains a lot of data that isn't needed to reconstruct the doc itself, including the entire model vocab. Using doc.to_bytes() will be a major improvement, and you can improve it a bit more by using exclude to exclude data that you don't need, like doc.tensor:
QUESTION
I 'm doing one project for analysing time series data. It's Apple stocks from 2018-1-1 to 2019-12-31. From the dataset, I selected two columns "Date" and "Ajd.close". I attached a small dataset here in below. (Alternatively: You can download the data directly from Yahoo finance. There is a download link under the blue button "Apply". )
I tested the dataset with adf.test(). It's not stationary. Now I would like to try another way, chunk the dataset into 24 periods(months), then compare the mean and variances of these chunked data. I tried with chunker() but it seems did not work. How should I do it? Thank you!
Here is a shorter version of the dataset:
...ANSWER
Answered 2021-Feb-14 at 16:58You could split the dataset and use map to make calculations on every chunck :
QUESTION
My goal is to chunk an array into blocks, and loop over those blocks in a for-loop. While looping, I would also like to print the percentage of the data that I have looped over so far (because in practice I'll be making requests on each loop, which will cause the loop to take a long time...)
Here is the code:
...ANSWER
Answered 2021-Jan-08 at 22:08chunked is a generator, not a list, so you can only iterate over it once. When you call list(chunked), it consumes the rest of the generator, so there's nothing left for the for loop to iterate over.
Also, len(list(chunked)) will be 1 less than you expect, since it doesn't include the current element of the iteration in the list.
Change chunker to use a list comprehension instead of returning a generator.
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