societybion.blogg.se - Compress data and recompress it to its original state

#Compress data and recompress it to its original state pdf
#Compress data and recompress it to its original state 32 bit
#Compress data and recompress it to its original state free

Back in 2014 we started with spinning disks under Kafka and never had issues with disk space. Having less network and disk usage was a big selling point for us. In reality, if you don't use encryption, data can be copied between NIC and disks with zero copies to user space, lowering the cost to some degree.

On the receiving side of the log only consumers need to decompress messages: The protocol itself is designed to minimize overheads as well, by requiring decompression only in a few places: The beauty of compression in Kafka is that it lets you trade off CPU vs disk and network usage. In practice this doesn't matter too much, because consumers usually read all records sequentially batch after batch. The downside here is that if you want to read record3 in the example above, you have to fetch records 1 and 2 as well, whether the batch is compressed or not. On the scale of thousands of messages difference becomes enormous. There's a high chance that records in the same Kafka topic share common parts, which means they can be compressed better. Now compression has a lot more space to do its job. In the previous log format messages recursive (compressed set of messages is a message), new format makes things more straightforward: compressed batch of records is just a batch. The naive approach to compression would be to compress messages in the log individually:Įdit: originally we said this is how Kafka worked before 0.11.0, but that appears to be false.Ĭompression algorithms work best if they have more data, so in the new log format messages (now called records) are packed back to back and compressed in batches. Just last year Kafka 0.11.0 came out with the new improved protocol and log format. Back in the old days we've tried enabling it a few times and ultimately gave up on the idea because of unresolved issues in the protocol. One of these improved areas was compression support.

While the idea of unifying abstraction of the log remained the same since then ( read this classic blog post from Jay Kreps if you haven't), Kafka evolved in other areas since then. We use Kafka as a log to power analytics (both HTTP and DNS), DDOS mitigation, logging and metrics. It's not that hard.We at Cloudflare are long time Kafka users, first mentions of it date back to beginning of 2014 when the most recent version was 0.8.0.

#Compress data and recompress it to its original state pdf

One particular way to that with dotImage is to pull out all the pages that are image only, recompress them and save them out to a new PDF then build a new PDF by taking all the pages from the original document and replacing them the recompressed pages, then saving again.

#Compress data and recompress it to its original state free

I will say that you can do most of this with Atalasoft dotImage (disclaimers: it's not free I work there I've written nearly all the PDF tools I used to work on Acrobat). That said, if you do can do all of this well in an unsupervised manner, you have a commercial product in its own right.

if you're a rock star, you want to try to segment the image and break it into areas that are really black and white and really color.

Most implementations of JPEG2000 and JBIG2 are not free. Flate is probably the best non-destructive compression for gray. If it's gray or black and white and contains a number of specks, consider despeckling. For example, if you run through a color/gray image and fine a lot of colors that cluster, consider smoothing them.

Examine the contents of the image to see if you can help make it more compressible.

if you have an rgb image that is really color, consider palettizing it.

measure the image dimensions in relation to how it is being placed on the page - if it's 300 dpi or greater, consider resampling the image to a smaller size depending on the bit depth of the image - for example, you can probably go from 300 dpi gray or rgb to 200 dpi and not lose too much detail.

If it's gray and there is relatively little in the way of gray variations, consider converting to 1 bit with a suitable binarization technique. If it's gray or paletted and only has 2 real colors, convert to 1-bit. If it's rgb and really gray, convert to gray.

determine if the image is really what it is.

#Compress data and recompress it to its original state 32 bit

If you have a 24-bit rgb or 32 bit cmyk image do the following: Here's an approach to do this (and this should work without regard to the toolkit you use):