sparkhydra | spark hydra this program is an application

by gurvindersingh Java Version: Current License: No License

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kandi X-RAY | sparkhydra Summary

sparkhydra is a Java library typically used in Big Data, Spark applications. sparkhydra has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

spark hydra this program is an application for performing proteomic search on a spark cluster. the program contains multiple search algorithms but most work has been done on a version of comet. the code depends on another github project - spark accumulators primarily to provide profiling support. the current version of the code depends on a spark 1.5 cluster. the command to run depends on two files - one describing the cluster and a second describing the proteomic search independent of the cluster. the command line looks line the following: spark-submit --class com.lordjoe.distributed.hydra.comet_spark.sparkcometscanscorer stevespark.jar ~/sparkclustereupag.properties input_searchgui_scan10000.xml where spark-submit - standard spark command --class com.lordjoe.distributed.hydra.comet_spark.sparkcometscanscorer define the main class hydraspark.jar define the jar file ~/sparkclustereupag.properties file decsribing the cluster

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Support

sparkhydra has a low active ecosystem.

It has 2 star(s) with 2 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of sparkhydra is current.

Quality

sparkhydra has no bugs reported.

Security

sparkhydra has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

sparkhydra 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.

Reuse

sparkhydra 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 sparkhydra and discovered the below as its top functions. This is intended to give you an instant insight into sparkhydra implemented functionality, and help decide if they suit your requirements.

Main entry point
Computes the sizes of each bin pair in each band
Calculates a Java RNNRDD for all peptides in Spark
Shows the analysis totals of the analysis
Register all classes
Registers a class with the given name
Score scan
Handle debug products
Compare two types
Determine if two scans are equivalent
Conditioned for testing
Parses the notes
Converts an abbreviated amino acid from an abbreviation string
Converts a spectrum to a raw spectrum
This method writes the values of the supported peaks
Main method to start counting statistics
Method to start the potential modulations
Sets the aromaticAcid masses
Main entry point for Spark
Add data to raw
Given a list of peptide points compute the distance between two amino acids and the intensity
Main scoring method
The main scoring function
Compute the dot product
Main launcher of SparkScorer
This method is used to perform the actual call to compute the curves

Get all kandi verified functions for this library.

sparkhydra Key Features

No Key Features are available at this moment for sparkhydra.

sparkhydra Examples and Code Snippets

No Code Snippets are available at this moment for sparkhydra.

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 sparkhydra

You can download it from GitHub.
You can use sparkhydra 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 sparkhydra 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 .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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