100k Have 8Million.txt VERIFIED

I have used pandas/dataframes too. I am able to do a bit of the processing using that. However, I want to be able to retain the original format of the file after processing which dataframes make a bit tricky as they return data in either ndarray format or acsv format.

100k Have 8Million.txt

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If you notice in your execution plan you are doing a COLLSCAN. With nReturned 1727172 and you are not examining any keys (totalKeysExamined: 0). This means that the query that you are using is not using an index. So I would take a look again at your indexes that you already have and the query that you are using and try to build a better index.

This don't really answer your question but if you are going to look into huge files I have found the gun to be the only working editor homepage looks like crap and editor was written ages ago in assembler and have few features, but it works.

I also think baretail can handle those huge files but is not 100% sure, baretail is also a tailer and have some features like filters and such. (if you just want the end of the file I assume you are going to analyze logs, can't se any other sane situation)

Doing it this way is really helpful because it bypasses loading the 8 millionrecords into the data grid UI and just writes them straight to disk. The resultis much faster performance with less system memory used. You would only belimited at this point by the space you have on your hard drive.

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So I'm really at my wit's end here, but I have a large dataset that I'm trying to import into R, but my computer takes hours to try and out it in before running out of memory and failing to process it. The file is a ndjson file and is a set of yelp reviews from their dataset challenge. As of now I have tried the following:

You have two separate problems: memory, and speed. The memory (RAM) of your computer is fixed, apparently you don't have enough to store all records at once, there is nothing you can do about that, so methods 1, 2, 3 won't work no matter what package you use.

(small technical note: there could be a way, let's say you can use 6GB for R and you have 5,000,000 records, that still gives 1 MB per record, so perhaps there would be a way to reduce the amount of data in one record when loading it to fit each one in less than 1 MB, which is roughly the size of 1,000 characters of text)

I've tried doing a readLines and it does work for the first 500k entries without a problem - the question now is, how do I read the subsequent batches of lines, from 500k to 1 million and etc? looking at the readLines documentation, you can only read the first x lines, or the the first total numbers - x lines. Once I have the data broken into dataframes of 500k its not a problem, but how do I make the next few? thanks!

Now you're running back into the previous problem: you don't have enough memory to store all the records simultaneously. The idea of the callback function is that it does processing and then only returns a result and discards all the data, freeing up memory for the next chunk. The callback function is a bit hard to define however, because it requires a specific formulation.

You obviously can't load all the json files in memory all at once, since all taken together they make up for 10 GB, which is more than your computer has. But if you only wanted to load "yelp_academic_dataset_checkin.json (428.83 MB)", you should be able to do it (provided you have no other big object in your R session, and you don't have some other software like a web browser that is using all your RAM). In that case you could try something like:

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Each failed document along with its document key (when available) will show up as an error in the indexer execution status. You can utilize the index api to manually upload the documents at a later point if you have set the indexer to tolerate failures.

The output mapping might have failed because the output data is in the wrong format for the mapping function you're using. For example, applying Base64Encode mapping function on binary data would generate this error. To resolve the issue, either rerun indexer without specifying mapping function or ensure that the mapping function is compatible with the output field data type. See Output field mapping for details.

If you encounter a timeout error with a custom skill, there are a couple of things you can try. First, review your custom skill and ensure that it's not getting stuck in an infinite loop and that it's returning a result consistently. Once you have confirmed that a result is returned, check the duration of execution. If you didn't explicitly set a timeout value on your custom skill definition, then the default timeout is 30 seconds. If 30 seconds isn't long enough for your skill to execute, you may specify a higher timeout value on your custom skill definition. Here's an example of a custom skill definition where the timeout is set to 90 seconds:

The maximum value that you can set for the timeout parameter is 230 seconds. If your custom skill is unable to execute consistently within 230 seconds, you may consider reducing the batchSize of your custom skill so that it will have fewer documents to process within a single execution. If you have already set your batchSize to 1, you'll need to rewrite the skill to be able to execute in under 230 seconds or otherwise split it into multiple custom skills so that the execution time for any single custom skill is a maximum of 230 seconds. Review the custom skill documentation for more information.

In all these cases, refer to Supported Data types and Data type map for indexers to make sure that you build the index schema correctly and have set up appropriate indexer field mappings. The error message will include details that can help track down the source of the mismatch.

This applies to SQL tables, and usually happens when the key is either defined as a composite key or, when the table has defined a unique clustered index (as in a SQL index, not an Azure Search index). The main reason is that the key attribute is modified to be a composite primary key in the case of a unique clustered index. In that case, make sure that your SQL table doesn't have a unique clustered index, or that you map the key field to a field that is guaranteed not to have duplicate values. 041b061a72