Spark-based-Ripley-K-Functions | visual analytics framework for Distributed Ripley

by ZPGuiGroupWhu JavaScript Version: Current License: No License

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kandi X-RAY | Spark-based-Ripley-K-Functions Summary

Spark-based-Ripley-K-Functions is a JavaScript library typically used in Big Data, Spark applications. Spark-based-Ripley-K-Functions has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

A visual analytics framework for Distributed Ripley's K functions based on Apache Spark, including Space K function, Space-Time K function, Local K function and Cross K function. This framework consists of three parts, i.e., front-end web visualization components, Web service API component and back-end Spark-based algorithm packages.

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Spark-based-Ripley-K-Functions has a low active ecosystem.

It has 21 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Spark-based-Ripley-K-Functions has no issues reported. There are 10 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Spark-based-Ripley-K-Functions is current.

Quality

Spark-based-Ripley-K-Functions has no bugs reported.

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Spark-based-Ripley-K-Functions has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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Spark-based-Ripley-K-Functions does not have a standard license declared.

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Without a license, all rights are reserved, and you cannot use the library in your applications.

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Spark-based-Ripley-K-Functions releases are not available. You will need to build from source code and install.

Top functions reviewed by kandi - BETA

kandi has reviewed Spark-based-Ripley-K-Functions and discovered the below as its top functions. This is intended to give you an instant insight into Spark-based-Ripley-K-Functions implemented functionality, and help decide if they suit your requirements.

D3 time format function
Computes the clip - line interceptor .
initializer for brush event
Creates a series
Make a clipping clip circle .
Computes a clip - point clipping method for the clipping point .
Returns D3 locale - number formatting function .
Divide a scale function
Resample stream
Calculates the quadtree of the given data

Get all kandi verified functions for this library.

Spark-based-Ripley-K-Functions Key Features

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Spark-based-Ripley-K-Functions Examples and Code Snippets

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Community Discussions

Trending Discussions on Big Data

How to group unassociated content

Using Spark window with more than one partition when there is no obvious partitioning column

What is the best way to store +3 millions records in Firestore?

spark-shell throws java.lang.reflect.InvocationTargetException on running

For function over multiple rows (i+1)?

Filling up shuffle buffer (this may take a while)

Designing Twitter Search - How to sort large datasets?

Unnest Query optimisation for singular record

handling million of rows for lookup operation using python

split function does not return any observations with large dataset

QUESTION

How to group unassociated content

Asked 2022-Apr-15 at 12:43

I have a hive table that records user behavior

like this

userid behavior timestamp url 1 view 1650022601 url1 1 click 1650022602 url2 1 click 1650022614 url3 1 view 1650022617 url4 1 click 1650022622 url5 1 view 1650022626 url7 2 view 1650022628 url8 2 view 1650022631 url9

About 400GB is added to the table every day.

I want to order by timestamp asc, then one 'view' is in a group between another 'view' like this table, the first 3 lines belong to a same group , then subtract the timestamps, like 1650022614 - 1650022601 as the view time.

How to do this?

i try lag and lead function, or scala like this

...

ANSWER

Answered 2022-Apr-15 at 12:43

If you use dataframe, you can build partition by using window that sum a column whose value is 1 when you change partition and 0 if you don't change partition.

You can transform a RDD to a dataframe with sparkSession.createDataframe() method as explained in this answer

Back to your problem. In you case, you change partition every time column behavior is equal to "view". So we can start with this condition:

Source https://stackoverflow.com/questions/71883786

QUESTION

Using Spark window with more than one partition when there is no obvious partitioning column

Asked 2022-Apr-10 at 20:21

Here is the scenario. Assuming I have the following table:

identifier line 51169081604 2 00034886044 22 51168939455 52

The challenge is to, for every single column line, select the next biggest column line, which I have accomplished by the following SQL:

...

ANSWER

Answered 2022-Apr-10 at 20:21

Using your "next" approach AND assuming the data is generated in ascending line order, the following does work in parallel, but if actually faster you can tell me; I do not know your volume of data. In any event you cannot solve just with SQL (%sql).

Here goes:

Source https://stackoverflow.com/questions/71803991

QUESTION

What is the best way to store +3 millions records in Firestore?

Asked 2022-Apr-09 at 13:18

I want to store +3 millions records in my Firestore database and I would like to know what is the best way, practice, to do that.

In fact, I want to store every prices of 30 cryptos every 15 minutes since 01/01/2020.

For example:

ETH price at 01/01/2020 at 00h00 = xxx
ETH price at 01/01/2020 at 00h15 = xxx
ETH price at 01/01/2020 at 00h30 = xxx
...
ETH price at 09/04/2022 at 14h15 = xxx

and this, for 30 cryptos (or more).

So, 120 prices per day multiplied by 829 days multiplied by 30 cryptos ~= 3M records

I thought of saving this like this:

[Collection of Crypto] [Document of crypto] [Collection of dates] [Document of hour] [Price]

I don't know if this is the right way, that's why I come here :)

Of course, the goal of this database will be to retrieve ALL the historical prices of a currency that I would have selected. This will allow me to make statistics etc later.

Thanks for your help

...

ANSWER

Answered 2022-Apr-09 at 13:18

For the current structure, instead of creating a document every 15 minutes you can just create a "prices" document and store an array of format { time: "00:00", price: 100 } which will cost only 1 read to fetch prices of a given currency on a day instead of 96.

Alternatively, you can create a single collection "prices" and create a document everyday for each currency. A document in this collection can look like this:

Source https://stackoverflow.com/questions/71808107

QUESTION

spark-shell throws java.lang.reflect.InvocationTargetException on running

Asked 2022-Apr-01 at 19:53

When I execute run-example SparkPi, for example, it works perfectly, but when I run spark-shell, it throws these exceptions:

...

ANSWER

Answered 2022-Jan-07 at 15:11

i face the same problem, i think Spark 3.2 is the problem itself

switched to Spark 3.1.2, it works fine

Source https://stackoverflow.com/questions/70317481

QUESTION

For function over multiple rows (i+1)?

Asked 2022-Mar-30 at 08:31

New to R, my apologies if there is an easy answer that I don't know of.

I have a dataframe with 127.124 observations and 5 variables

Head(SortedDF)

...

ANSWER

Answered 2022-Mar-30 at 08:31

library(tidyverse)

data <- tibble(x = c(1, 1, 2), y = "a")
data
#> # A tibble: 3 × 2
#>       x y    
#>    
#> 1     1 a    
#> 2     1 a    
#> 3     2 a

same_rows <-
  data %>%
  # consider all columns
  unite(col = "all") %>%
  transmute(same_as_next_row = all == lead(all))

data %>%
  bind_cols(same_rows)
#> # A tibble: 3 × 3
#>       x y     same_as_next_row
#>                
#> 1     1 a     TRUE            
#> 2     1 a     FALSE           
#> 3     2 a     NA

Source https://stackoverflow.com/questions/71673259

QUESTION

Filling up shuffle buffer (this may take a while)

Asked 2022-Mar-28 at 20:44

I have a dataset that includes video frames partially 1000 real videos and 1000 deep fake videos. each video after preprocessing phase converted to the 300 frames in other worlds I have a dataset with 300000 images with Real(0) label and 300000 images with Fake(1) label. I want to train MesoNet with this data. I used costum DataGenerator class to handle train, validation, test data with 0.8,0.1,0.1 ratios but when I run the project show this message:

...

ANSWER

Answered 2021-Nov-10 at 14:23

Note that this is not an error, but a log message: https://github.com/tensorflow/tensorflow/blob/42b5da6659a75bfac77fa81e7242ddb5be1a576a/tensorflow/core/kernels/data/shuffle_dataset_op.cc#L138

It seems you may be choosing too large a dataset if it's taking too long: https://github.com/tensorflow/tensorflow/issues/30646

You can address this by lowering your buffer size: https://support.huawei.com/enterprise/en/doc/EDOC1100164821/2610406b/what-do-i-do-if-training-times-out-due-to-too-many-dataset-shuffle-operations

Source https://stackoverflow.com/questions/69914867

QUESTION

Designing Twitter Search - How to sort large datasets?

Asked 2022-Mar-24 at 17:25

I'm reading an article about how to design a Twitter Search. The basic idea is to map tweets based on their ids to servers where each server has the mapping

English word -> A set of tweetIds having this word

Now if we want to find all the tweets that have some word all we need is to query all servers and aggregate the results. The article casually suggests that we can also sort the results by some parameter like "popularity" but isn't that a heavy task, especially if the word is an hot word?

What is done in practice in such search systems?

Maybe some tradeoff are being used?

Thanks!

...

ANSWER

Answered 2022-Mar-24 at 17:25

First of all, there are two types of indexes: local and global.

A local index is stored on the same computer as tweet data. For example, you may have 10 shards and each of these shards will have its own index; like word "car" -> sorted list of tweet ids.

When search is run we will have to send the query to every server. As we don't know where the most popular tweets are. That query will ask every server to return their top results. All of these results will be collected on the same box - the one executing the user request - and that process will pick top 10 of of entire population.

Since all results are already sorted in the index itself, it is a O(1) operation to pick top 10 results from all lists - as we will be doing simple heap/watermarking on set number of tweets.

Second nice property, we can do pagination - the next query will be also sent to every box with additional data - give me top 10, with popularity below X, where X is the popularity of last tweet returned to customer.

Global index is a different beast - it does not live on the same boxes as data (it could, but does not have to). In that case, when we search for a keyword, we know exactly where to look for. And the index itself is also sorted, hence it is fast to get top 10 most popular results (or get pagination).

Since the global index returns only tweet Ids and not tweet itself, we will have to lookup tweets for every id - this is called N+1 problem - 1 query to get a list of ids and then one query for every id. There are several ways to solve this - caching and data duplication are by far most common approaches.

Source https://stackoverflow.com/questions/71588238

QUESTION

Unnest Query optimisation for singular record

Asked 2022-Mar-24 at 11:45

I'm trying to optimise my query for when an internal customer only want to return one result *(and it's associated nested dataset). My aim is to reduce the query process size.

However, it appears to be the exact same value regardless of whether I'm querying for 1 record (with unnested 48,000 length array) or the whole dataset (10,000 records with unnest total 514,048,748 in total length of arrays)!

So my table results for one record query:

...

ANSWER

Answered 2022-Mar-24 at 11:45

This is happening because there is still need for a full table scan to find all the test IDs that are equal to the specified one.

It is not clear from your example which columns are part of the timeseries record. In case test_id is not one of them, I would suggest to cluster the table on the test_id column. By clustering, the data will be automatically organized according to the contents of the test_id column.

So, when you query with a filter on that column a full scan won't be needed to find all values.

Vulnerabilities

No vulnerabilities reported

Install Spark-based-Ripley-K-Functions

You can download it from GitHub.

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