![]() ![]() zlib) and therefore a user of one either view file names on and commands line. In benchmark employing a given file, give the compression optimizing you want to test Snappy against (e.g. In addition, you must include the expired parameter to check the value of the Expires header field. snappytesttool can benchmark Snappy against a few misc compression libraries (zlib, LZO, LZF, or QuickLZ), if they were nachgewiesen at configure time. For this purpose the gzip_proxied directive has parameters that instruct NGINX to check the Cache-Control header field in a response and compress the response if the value is no-cache, no-store, or private. The main competitors are Snappy 1 (currently being the key part of the Google infrastructure. ![]() For example, it is reasonable to compress responses only to requests that will not be cached on the proxy server. There are several other high-speed compression algorithms. The directive has a number of parameters specifying which kinds of proxied requests NGINX should compress. To configure compression of these responses, use the gzip_proxied directive. The only optional setting is DATA COMPRESSION, which can use Google's Snappy compression format or the default zlib format. The fact that a request comes from a proxy server is determined by the presence of the Via header field in the request. The same compilation settings worked for gzip.By default, NGINX does not compress responses to proxied requests (requests that come from the proxy server). However, I tested this on a single machine compressing and decompressing a local file. Never figured out why LZ4 doesn't work, as suggested in the comments this could be an endianess problem or a 64/32-bit mismatch. If anybody interested I ended up using gzip from zlib. For this purpose the gzipproxied directive. A buffer is roughly posted 2 times a second which creates a traffic of about 2Kb/sec. For example, it is reasonable to compress responses only to requests that will not be cached on the proxy server. The whole OS process shall take less than 5% of the machine's CPU. This code represents an example of running the Snappy software compression with timing. Client collects some near real time data into a small buffer, when that buffer gets full it shall be compressed and POSTed. The current version of the Snappy algorithm as defined. I plan to try gzip and maybe deflate, but I am not sure if I can configure compression levels there or whether it will work in both C/C++ and C#. The size of the buffer must be at least as big as the biggest offset used in the compressed stream. Has anyone tried using a fast C/C++ compression and C# decompression? Any recommendations?Ĭlient application shall not use more than 5% of CPU - that's a vague requirement I have. However, the format used 30 CPU while GZIP used 58. Using the same file foo.csv with GZIP results in a final file size of 1.5 MB. ![]() I also checked LZ4, where compression/decompression works in a single language, but when I try to use both - I cannot decompress data correctly (the decompressed bytes are set to 0s). For example, running a basic test with a 5.6 MB CSV file called foo.csv results in a 2.4 MB Snappy file. I have tried using Google Snappy, there is only a C/C++ version and any. I cannot decompress raw bytes and I end up with decompressor's output buffers stay intact. On the server I need to decompress raw bytes and get a string. import snappy def snappycompress (path): pathtostore path+. 1 I am trying to compress in snappy format a csv file using a python script and the python-snappy module. They are based on the same underlying algorithm but they aren't compatible in that you can compress with one and decompress with another. This function will compress a file using framed method. 4 Answers Sorted by: 3 The issue here is that python-snappy is not compatible with Hadoop's snappy codec, which is what Spark will use to read the data when it sees a '.snappy' suffix. There are two ways to compress snappy data - as one block and the other as streaming (or framed) data. The data is a unicode string that I need to compress using any fast compression algorithm (needs to be light on CPU). You are compressing the plain string, as the compress function takes raw data. I need to HTTP POST some data to the server. The client is written in C++ (working on Windows, planning to support Linux) and the server is a. I am working on a client-server application. ![]()
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