large-data

问题 This question already has answers here : How to aggregate values into collection after groupBy? (3 answers) Closed 8 months ago . I have a dataset like below; with values of col1 repeating multiple times and unique values of col2. This original dataset can almost a billion rows, so I do not want to use collect or collect_list as it will not scale-out for my use case. Original Dataset: +---------------------| | col1 | col2 | +---------------------| | AA| 11 | | BB| 21 | | AA| 12 | | AA| 13 | |

问题 I have two large .csv files that I've imported using the import-csv cmdlet. I've done a lot of searching and trying and am finally posting to ask for some help to make this easier. I need to move through the first array that will have anywhere from 80k rows to 500k rows. Each object in these arrays has multiple properties, and I then need to find the corresponding entry in a second array of the same size matching on a property from there. I'm importing them as [systems.collection.arrayList]

问题 I have two large .csv files that I've imported using the import-csv cmdlet. I've done a lot of searching and trying and am finally posting to ask for some help to make this easier. I need to move through the first array that will have anywhere from 80k rows to 500k rows. Each object in these arrays has multiple properties, and I then need to find the corresponding entry in a second array of the same size matching on a property from there. I'm importing them as [systems.collection.arrayList]

问题 I want to insert real-time logging data into Neo4j 2.2.1 through Spring Data Neo4j 4.0.0. The logging data is very big which may reach hundreds of thousands records. How is the best way to implement this kind of functionality? Is it safe to just using the .save(Iterable) method at the end of all the node entity objects creation? Is there something like batch insertion mechanism in Spring Data Neo4j 4.0.0? Thanks in advance! 回答1: As SDN4 can work with existing databases directly you can use

问题 I have a MySQL database with over 20 tables, but one of them is significantly large because it collects measurement data from different sensors. It's size is around 145 GB on disk and it contains over 1 billion records. All this data is also being replicated to another MySQL server. I'd like to separate the data to smaller "shards", so my question is which of the below solutions would be better. I'd use the record's "timestamp" for dividing the data by years. Almost all SELECT queries that

问题 I have a MySQL database with over 20 tables, but one of them is significantly large because it collects measurement data from different sensors. It's size is around 145 GB on disk and it contains over 1 billion records. All this data is also being replicated to another MySQL server. I'd like to separate the data to smaller "shards", so my question is which of the below solutions would be better. I'd use the record's "timestamp" for dividing the data by years. Almost all SELECT queries that

问题 I was writing a test for handling huge amounts of data. To my surprise, if I added a setTimeout to my function, it would no longer lead to a stack overflow (how appropriate for this site). How is this possible, the code seems to be really recursive. Is every setTimeout call creating it's own stack? Is there way to achieve this behavior (handle a huge array/number asynchronous and in order) without increasing the needed memory? function loop( left: number, callbackFunction: (callback: () =>

问题 Given a numpy.memmap object created with mode='r' (i.e. read-only), is there a way to force it to purge all loaded pages out of physical RAM, without deleting the object itself? In other words, I'd like the reference to the memmap instance to remain valid, but all physical memory that's being used to cache the on-disk data to be uncommitted. Any views onto to the memmap array must also remain valid. I am hoping to use this as a diagnostic tool, to help separate "real" memory requirements of a

问题 I have an array of size 10000. All those are document ids. I am running an array and need to get the document data from Firestone and need to add the new fields in each document. But I am facing the below errors like @firebase/firestore: Firestore (5.3.1): FirebaseError: [code=resource-exhausted]: Resource has been exhausted (e.g. check quota). @firebase/firestore: Firestore (5.3.1): Using maximum backoff delay to prevent overloading the backend. Error getting document: FirebaseError: Failed

问题 I want to read data stored in text format in a 5GB file. when I try to read the content of file using this code: file = open('../data/entries_en.txt', 'r') data = file.readlines() an error occurred: data = file.readlines() MemoryError My laptop has 8GB memory and at least 4GB is empty when I want to run the program. but when I monitor the system performance, when python uses about 1.5GB of memory, this error happens. I'm using python 2.7, but if it matters please tell me solution for 2.x and