Architecture for a Deduplicated Archival Store: Part 1 ().Storage Myths: Dedupe for Databases ().Also an efficient partitioned indexing is needed to be able to scale deduplication in a cluster without losing deduplication ratio. It needs work to turn it into an archival store where data can be ingested incrementally and selective data streams extracted for restore. However the big missing piece in all this is that Pcompress is still a stand-alone utility. In addition to the above, the recent zip-like archiver capabilities that I have added into Pcompress introduce data type detection and automatic selection of filters and compression techniques to suit the data type. I have posted about a few of these earlier. There are other unique techniques in Pcompress like a partially vectorized rolling hash, scanning less than 30% of the data to locate chunk boundaries, parallelized deduplication and others that contribute to the overall performance. It avoids any kind of complicated math, fuzzy hashing etc.I will detail it later. The similarity detection technique is simple and novel. The chunks lists of those regions are then sequentially loaded from SSD and compared to perform actual deduplication. The approach is based on similarity detection of large regions in the data stream. The metadata structure that I have discussed in earlier posts also helps with overall performance. This single capability solves a sticky problem that dedupe solutions has been dealing with for quite some time.
ZERO BYTE FILE WORDPRESS FULL
This deduplication occurs with 95% efficiency of a full chunk index based brute-force dedupe. If the data has duplicates then the index size becomes smaller. With 2KB chunks it can potentially scale to 500TB of 100% random data using a 40GB in-memory global index. Pcompress can use blocks (or chunks) as small as 2KB without losing too much performance. This is a key feature that most of the current solutions seem to be missing. However the other “ new aspect in Pcompress is the ability to use small blocks for deduplication without losing performance and giving good scalability“. Fast metadata access is critical and this is the logical choice.
In some of the posts on Pcompress design I have already mentioned about using SSDs for storing all kinds of metadata. Now the question is how efficient restore can be when delta-differencing is used. I am not sure what this “byte-level deduplication” exactly means but it appears to be a delta-differencing mechanism. The other interesting features include byte-level deduplication for databases that store data in block sizes less than 8KB and using flash based SSDs to store the global index. Sepaton can do analysis of data types and change rates in data to apply the most efficient dedupe mechanism or even skip dedupe for encrypted and compressed files that virtually do not deduplicate at all.
ZERO BYTE FILE WORDPRESS WINDOWS
It has also been used effectively in Windows Server 2012 to do primary data deduplication. Post-process deduplication is a common technique to scale deduplication without affecting I/O throughput of in-flight data. This is of course to not hurt backup performance and scale to large datasets. Secondly it can deduplicate using large chunks during backup ingestion and then dedupe using small blocks as post-process. Firstly it can detect duplicate files during ingestion, store only a single copy and then do block-level dedupe as post-process.
The article is not clear what exactly this means. It mixes “Hash-based inline deduplication” and “Post-process, content-aware deduplication”. If I look at the deduplication features a few things look interesting vis-a-vis those that I have put into Pcompress. Potentially handles upto 16PB of backup storage with Global Deduplication – Great! The performance and features on paper are really top of the line. Came across this news piece on Register dated back to October:
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