marmaray | Generic Data Ingestion & Dispersal Library for Hadoop
kandi X-RAY | marmaray Summary
kandi X-RAY | marmaray Summary
Marmaray is a generic Hadoop data ingestion and dispersal framework and library. It is a plug-in based framework built on top of the Hadoop ecosystem where support can be added to ingest data from any source and disperse to any sink leveraging the power of Apache Spark. Marmaray describes a number of abstractions to support the ingestion of any source to any sink. They are described at a high-level below to help developers understand the architecture and design of the overall system. This system has been canonically used to ingest data into a Hadoop data lake and disperse data from a data lake to online data stores usually with lower latency semantics. The framework was intentionally designed, however, to not be tightly coupled to just this particular use case and can move data from any source to any sink. The figure below illustrates a high level flow of how Marmaray jobs are orchestrated, independent of the specific source or sink. During this process, a configuration defining specific attributes for each source and sink orchestrates every step of the next job. This includes figuring out the amount of data we need to process (i.e., its Work Unit), applying forking functions to split the raw data, for example, into ‘valid’ and ‘error’ records and converting the data to an appropriate sink format. At the end of the job the metadata will be saved/updated in the metadata manager, and metrics can be reported to track progress. The following sections give an overview of each of the major components that enable the job flow previously illustrated. The architecture diagram below illustrates the fundamental building blocks and abstractions in Marmaray that enable its overall job flow. These generic components facilitate the ability to add extensions to Marmaray, letting it support new sources and sinks. The central component of Marmaray’s architecture is what we call the AvroPayload, a wrapper around Avro’s GenericRecord binary encoding format which includes relevant metadata for our data processing needs. One of the major benefits of Avro data (GenericRecord) is that it is efficient both in its memory and network usage, as the binary encoded data can be sent over the wire with minimal schema overhead compared to JSON. Using Avro data running on top of Spark’s architecture means we can also take advantage of Spark’s data compression and encryption features. These benefits help our Spark jobs more efficiently handle data at a large scale. To support our any-source to any-sink architecture, we require that all ingestion sources define converters from their schema format to Avro and that all dispersal sinks define converters from the Avro Schema to the native sink data model (i.e., ByteBuffers for Cassandra). Requiring that all converters either convert data to or from an AvroPayload format allows a loose and intentional coupling in our data model. Once a source and its associated transformation have been defined, the source theoretically can be dispersed to any supported sink, since all sinks are source-agnostic and only care that the data is in the intermediate AvroPayload format.
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Top functions reviewed by kandi - BETA
- Calculate work units
- Calculate offset ranges to read offsets for a given partition
- Compute the run metrics
- Gets the available topic partitions
- Converts an AvroRDD to Cassandra
- Gets the hooks
- Setup the Cassandra table for the given keyspace
- Write error records to an error table
- Convert data from an AvroRDD to RDD
- Synchronously saves job information to Cassandra cluster
- Entry point for testing purposes
- Converts an Avro schema into SQL Schema
- Checks if the schema is valid
- Add a field
- Writes records to error table
- Read data from Hive source
- Finish execution of a given tier
- Get topic partition offsets
- Sorts a list of JobDags in the input queue
- Convert an Avro schema into a cassandra schema
- Method to write data to hdfs folder
- Get Avro data from Kafka
- List files under a given path
- Write Avro payload to Cassandra
- Converts a Row to Avro schema
- Main method for testing
marmaray Key Features
marmaray Examples and Code Snippets
Community Discussions
Trending Discussions on Big Data
QUESTION
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 url9About 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 12:43If 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:
QUESTION
Here is the scenario. Assuming I have the following table:
identifier line 51169081604 2 00034886044 22 51168939455 52The 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 20:21Using 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:
QUESTION
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 13:18For 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:
QUESTION
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 15:11i face the same problem, i think Spark 3.2 is the problem itself
switched to Spark 3.1.2, it works fine
QUESTION
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 08:31library(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
QUESTION
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 14:23Note 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
QUESTION
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 17:25First 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.
QUESTION
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 11:45This 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.
Read more about clustered tables here.
QUESTION
I am new to data handling . I need to create python program to search a record from a samplefile1 in samplefile2. i am able to achieve it but for each record out of 200 rows in samplefile1 is looped over 200 rows in samplefile2 , it took 180 seconds complete execution time.
I am looking for something to be more time efficient so that i can do this task in minimum time .
My actual Dataset size is : 9million -> samplefile1 and 9million --> samplefile2.
Here is my code using Pandas.
sample1file1 rows:
...ANSWER
Answered 2022-Mar-19 at 11:27I don't think using Pandas is helping here as you are just comparing whole lines. An alternative approach would be to load the first file as a set of lines. Then enumerate over the lines in the second file testing if it is in the set. This will be much faster:
QUESTION
I have a dataframe like this:
...ANSWER
Answered 2022-Mar-12 at 22:29It is just that there are many unused levels as the column 'seqnames' is a factor. With split, there is an option to drop (drop = TRUE - by default it is FALSE) to remove those list elements. Otherwise, they will return as data.frame with 0 rows. If we want those elements to be replaced by NULL, then find those elements where the number of rows (nrow) are 0 and assign it to NULL
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install marmaray
You can use marmaray 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 marmaray 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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