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*Support for highly efficient insertions and deletions
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* Space efficient
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* Performance is comparable to hash tables
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* Maintains the data in sorted order, which enables additional operations like range scan and prefix lookup
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* O(k) search/insert/delete operations, where is k is the length of the key
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*`O(k)` search/insert/delete operations, where `k` is the length of the key
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* Minimum / Maximum value lookups
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* Ordered iteration
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* Prefixbased iteration
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* Support keys with null bytes, any byte array could be a key
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* Prefix-based iteration
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* Support for keys with null bytes, any byte array could be a key
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# Usage
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@@ -61,14 +61,13 @@ Check out the documentation on [godoc.org](http://godoc.org/github.com/plar/go-a
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# Performance
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The go-adaptive-radix-tree library overperforms go-art library.
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The go-adaptive-radix-tree doesn't allocate any memory during search operations.
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[plar/go-adaptive-radix-tree](https://github.com/plar/go-adaptive-radix-tree) outperforms [kellydunn/go-art](https://github.com/kellydunn/go-art) by avoiding memory allocations during search operations.
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It also provides prefix based iteration over the tree.
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Benchmarks were performed on datasets extracted from different projects:
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- Words dataset contains list of 235,886 english words. [2]
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- UUIDs dataset contains 100,000 uuids. [2]
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- HSK Words dataset contains 4,995 words. [4]
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-The "Words" dataset contains a list of 235,886 english words. [2]
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-The "UUIDs" dataset contains 100,000 uuids.[2]
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-The "HSK Words" dataset contains 4,995 words.[4]
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|**go-adaptive-radix-tree**| # | Average time |Bytes per operation|Allocs per operation |
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