is gson faster than jackson?
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Gson 1.6 now includes a low-level streaming API and a new parser which is actually faster than Jackson. Adding to other answers already given above. If case insensivity is of any importance to you, then use Jackson. Gson does not support case insensitivity for key names, while jackson does.
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In Retrofit, you configure which converter is used for the data serialization. Typically for JSON, you use GSon, but you can add custom converters to process XML or other protocols. Retrofit uses the OkHttp library for HTTP requests. You should use retrofit over okhttp for its ease, for its features.
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Conclusions. Yes, NSArray is faster than NSSet for simply holding and iterating. As little as 50% faster for constructing and as much as 500% faster for iterating. ... Even if you need both iteration and inclusion testing, you should probably still choose an NSSet.
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2 Answers. Two approaches: Implement a hashcode for your objects, and compare the hashcode of the in-memory objects against the hashcode of the serialized objects to see if they've been changed. This is has a low impact on your class design, but performance will go down as O(n^2) as the number of objects increases.
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List comprehension is faster because it is optimized for the Python interpreter to spot a predictable pattern during looping. Besides the syntactic benefit of list comprehensions, they are often as fast or faster than equivalent use of map .
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PLS_INTEGER operations use machine arithmetic, so they are generally faster than NUMBER and INTEGER operations. Also, prior to Oracle Database 10g, they are faster than BINARY_INTEGER. In Oracle Database 10g, however, BINARY_INTEGER and PLS_INTEGER are now identical and can be used interchangeably.
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The seek time for data is ten times faster. NVMe can deliver sustained read-write speed of 2000MB per second, way faster than the SATA SSD III, which limits at 600MB per second. Here the bottleneck is NAND technology, which is rapidly advancing, which means we'll likely see higher speeds soon with NVMe.
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2 Answers. JSON has a schema. REST services have WADL. Also there are tools like wadl2java .
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Gson instances are Thread-safe so you can reuse them freely across multiple threads. You can create a Gson instance by invoking new Gson() if the default configuration is all you need.
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NOTE: ToArray() is similar to ToList() but returning an array instead of a list and I included it in the benchmarks. Not surprisingly all the implementations are O(n), meaning the processing time increases with the number of items in the sequence. Surprisingly, ToArray() is faster than not using any conversion.
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The writing speed of the 850 EVO and 860 EVO SSD is exactly the same. The maximum read speed of the 860 EVO is 550 MB / s and that of the 850 EVO is 540 MB / s. This makes the 860 EVO slightly faster than its predecessor.
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CLion is written in Java and even though they do a great job at hiding that fact, meaning the application doesn't feel nor look like your standard cheap Java application, the resource allocation regarding RAM and CPU time is something you might not want to overlook.
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In the example of the micro-benchmark given, the reason the Enumeration is faster than Iterator is because it is tested first. ;) The longer answer is that the HotSpot compiler optimises a whole method when a loop has iterated 10,000 times.
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AGP is way better than PCI. AGP = 2.2 Gigabytes per second. PCI = 133 megabytes per second. PCI runs at 33MHZ and AGP runs at 66MHZ.
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Lodash is extremely well-optimized as far as modern JS goes, but, as you may know, nothing can be faster than native implementation. Even if Lodash uses the same API under-the-hood, function call overhead is still present. Some might say that these are just some micro-optimizations.
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You can parse the JSONArray directly, don't need to wrap your Post class with PostEntity one more time and don't need new JSONObject(). toString() either: Gson gson = new Gson(); String jsonOutput = "Your JSON String"; Type listType = new TypeToken<List<Post>>(){}. getType(); List<Post> posts = gson.
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As the array size increase, Numpy gets around 30 times faster than Python List. Because the Numpy array is densely packed in memory due to its homogeneous type, it also frees the memory faster.