json   39732

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jq
Command line tool for processing JSON data files.
json  tool  cli 
9 hours ago by erdman
jq
jq is like sed for JSON data – you can use it to slice and filter and map and transform structured data with the same ease that sed, awk, grep and friends let you play with text.

jq is written in portable C, and it has zero runtime dependencies. You can download a single binary, scp it to a far away machine, and expect it to work.
json  parsers  sed  awk 
10 hours ago by harrylove
Ahmed-Ali/JSONExport
JSONExport is a desktop application for Mac OS X which enables you to export JSON objects as model classes with their associated constructors, utility methods, setters and getters in your favorite language.
Swift  JSON  MacApp 
13 hours ago by nanoxd
Mockaroo - Random Data Generator | CSV / JSON / SQL / Excel
Mockaroo lets you generate up to 1,000 rows of realistic test data in CSV, JSON, SQL, and Excel formats.
programming  work  json  generator  test  data 
18 hours ago by bfolberth
JSON-to-Go: Convert JSON to Go instantly
type Autogenerated struct {
ReferenceEnd int64 `json:"reference_end"`
ReferenceStart int64 `json:"reference_start"`
Start int64 `json:"start"`
IsAnomalous bool `json:"is_anomalous"`
Set []struct {
Metrictype string `json:"metricType"`
Assetid string `json:"assetId"`
Assettype string `json:"assetType"`
} `json:"set"`
Reference []struct {
Metrictype string `json:"metricType"`
Assetid string `json:"assetId"`
Assettype string `json:"assetType"`
} `json:"reference"`
End int64 `json:"end"`
}
golang  json 
yesterday by shillcock
Defiant.js
Do you need to query large JSON structures? Do you end up coding loops to parse the JSON and identify the data that matches your query? DefiantJS offers a better way. DefiantJS extends the global JSON object with a "search" method that enables lightning-fast searches using XPath expressions. Try out the XPath Evaluator below to get the idea.
json  regexp  parse  xpath  search  javascript 
yesterday by guillehorno
Profiling Data Throughput
Common solutions for large semi-structured data include Python iterators, multiprocessing, Hadoop, and Spark as well as proper databases like MongoDB and ElasticSearch. Two months ago we built dask.bag, a toy dask experiment for semi-structured data. Today we’ll strengthen the dask.bag project and look more deeply at performance in this space.

We measure performance with data bandwidth, usually in megabytes per second (MB/s). We’ll build intuition for why dealing with this data is costly.
python  pandas  json  etl  data  performance  elasticsearch 
yesterday by euler

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