I'm working on a specific project where external data provided by external providers is to be indexed on our ElasticSearch Engine.
The data is provided as XML flat files.
The idea here is to script something out that reads each file, parse it and launch as many HTTP POST as needed for each one of them.
Is there a simpler way to do this? something like uploading the XML file that gets indexed automatically without any script?
You can use logstash with an xml filter to do this. Takes a bit of work to get setup the first time, but it's the most straightforward way to do it.
Related
I'm building an architecture using boto3, and I hope to dump the data in JSON format from API to S3. What blocks in my way right now is first, firehose does NOT support JSON; my workaround right now is not compressing them but it's still different from a JSON file. But I still want to see a better choice to make the files more compatible.
And second, the file names can't be customized. All the data I collected will be eventually converted onto Athena for the query, so can boto3 do the naming?
Answering a couple of the questions you have. Firstly if you stream JSON into Firehose it will write JSON to S3. JSON is the file data structure and compression is the file type. Compressing JSON doesn't make it something else. You'll just need to decompress it before consuming it.
RE: file naming, you shouldn't care about that. Let the system name it whatever. If you define the Athena table with the location, you'll be able to query it. When new files are added, you'll be able to query them immediately.
Here is an AWS tutorial that walks you through this process. JSON stream to S3 with Athena query.
I'm looking for ideas for an Open Source ETL or Data Processing software that can monitor a folder for CSV files, then open and parse the CSV.
For each CSV row the software will transform the CSV into a JSON format and make an API call to start a Camunda BPM process, passing the cell data as variables into the process.
Looking for ideas,
Thanks
You can use a Java WatchService or Spring FileSystemWatcher as discussed here with examples:
How to monitor folder/directory in spring?
referencing also:
https://www.baeldung.com/java-nio2-watchservice
Once you have picked up the CSV you can use my example here as inspiration or extend it: https://github.com/rob2universe/csv-process-starter specifically
https://github.com/rob2universe/csv-process-starter/blob/main/src/main/java/com/camunda/example/service/CsvConverter.java#L48
The example starts a configurable process for every row in the CSV and includes the content of the row as a JSON process data.
I wanted to limit the dependencies of this example. The CSV parsing logic applied is very simple. Commas in the file may break the example, special characters may not be handled correctly. A more robust implementation could replace the simple Java String .split(",") with an existing CSV parser library such as Open CSV
The file watcher would actually be a nice extension to the example. I may add it when I get around to it, but would also accept a pull request in case you fork my project.
I'm working on an ETL job that will ingest JSON files into a RDS staging table. The crawler I've configured classifies JSON files without issue as long as they are under 1MB in size. If I minify a file (instead of pretty print) it will classify the file without issue if the result is under 1MB.
I'm having trouble coming up with a workaround. I tried converting the JSON to BSON or GZIPing the JSON file but it is still classified as UNKNOWN.
Has anyone else run into this issue? Is there a better way to do this?
I have two json files which are 42mb and 16mb, partitioned on S3 as path:
s3://bucket/stg/year/month/_0.json
s3://bucket/stg/year/month/_1.json
I had the same problem as you, crawler classification as UNKNOWN.
I were able to solved it:
You must create custom classifier with jsonPath as "$[*]" then create new crawler with the classifier.
Run your new crawler with the data on S3 and proper schema will be created.
DO NOT update your current crawler with the classifier as it won't apply the change, I don't know why, maybe because of classifier versioning AWS mentioned in their documents. Create new crawler make them work
As mentioned in
https://docs.aws.amazon.com/glue/latest/dg/custom-classifier.html#custom-classifier-json
When you run a crawler using the built-in JSON classifier, the entire file is used to define the schema. Because you don’t specify a JSON path, the crawler treats the data as one object, that is, just an array.
That is something which Dung also pointed out in his answer.
Please also note that file encoding can lead to JSON being classified as UNKNOWN. Please try and re-encode the file as UTF-8.
Im trying out the MarkLogic Java API and would want to bulk upload some files with the extension .csv
I'm not sure what to use, since the Java API only supports JSON, XML, and TXT files.
How do I batch upload files using the MarkLogic Java api? Do i convert everything to JSON?
Do i convert everything to JSON?
Yes, that is a common way to do it.
If you would like additional examples of how you can wrangle CSV with the Java Client API, check out OpenCSVBatcherExample and JacksonDatabindTest.testDatabindingThirdPartyPojoWithMixinAnnotations. The first demonstrates converting the csv to XML and using a custom REST extension. The second example (well, unit test...) demonstrates converting the csv to JSON and using the batch upload (Bulk Writes) capabilities Justin linked to.
If you have CSV files on your filesystem, I’d start with mlcp, as suggested above. It will handle all of the parsing and splitting into multiple transactions/batches for you. Take a look at the mlcp documentation for more details and some example configurations.
If you’d like more control over the parsing and splitting logic than mlcp gives you out-of-the-box or you’re getting CSV from some other source (i.e. not files on the filesystem), you can use the Java Client API. The Java Client API allows you to efficiently write batches using a WriteSet. Take a look at the “Bulk Writes” example.
According to your reply to Justin, you cannot use MLCP because it is command line and you need to integrate it into a web portal.
Well, MLCP is released as open cource software under the Apache2 licence. So if you are happy with this licence, then you have the source to integrate.
But what I see as your main problem statement is more specific:
How can I create miltiple XML OR JSON documents from a CSV file [allowing the use of the java API to then upload them as documents in MarkLogic]
With that specific problem statement:
1) have a look at SplitDelimitedTextReader.java from the mlcp source
2) try some java libraries for this purpose such as http://jsefa.sourceforge.net/quick-tutorial.html
I am trying to find the best way to import all of our Lighthouse data (which I exported as JSON) into JIRA, which wants a CSV file.
I have a main folder containing many subdirectories, JSON files and attachments. The total size is around 50MB. JIRA allows importing CSV data so I was thinking of trying to convert the JSON data to CSV, but all convertors I have seen online will only do a file, rather than parsing recursively through an entire folder structure, nicely creating the CSV equivalent which can then be imported into JIRA.
Does anybody have any experience of doing this, or any recommendations?
Thanks, Jon
The JIRA CSV importer assumes a denormalized view of each issue, with all the fields available in one line per issue. I think the quickest way would be to write a small Python script to read the JSON and emit the minimum CSV. That should get you issues and comments. Keep track of which Lighthouse ID corresponds to each new issue key. Then write another script to add things like attachments using the JIRA SOAP API. For JIRA 5.0 the REST API is a better choice.
We just went through a Lighthouse to JIRA migration and ran into this. The best thing to do is in your script, start at the top-level export directory and loop through each ticket.json file. You can then build a master CSV or JSON file to import into JIRA that contains all tickets.
In Ruby (which is what we used), it would look something like this:
Dir.glob("path/to/lighthouse_export/tickets/*/ticket.json") do |ticket|
JSON.parse(File.open(ticket).read).each do |data|
# access ticket data and add it to a CSV
end
end