For this reason, SSD controllers use a technique called wear leveling to distribute writes as evenly as possible across all the flash blocks in the SSD. If the data is mixed in the same blocks, as with almost all systems today, any rewrites will require the SSD controller to garbage collect both the dynamic data which caused the rewrite initially and static data which did not require any rewrite.
The key is to find an optimum algorithm which maximizes them both. If the SSD has a high write amplification, the controller will rocksdb write amplification required to write that many more times to the flash memory. This reduces the LBAs needing to be moved during garbage collection.
If the OS determines that file is to be replaced or deleted, the entire block can be marked as invalid, and there is no need to read parts of it to garbage rocksdb write amplification and rewrite into another block.
It will need only to be erased, which is much easier and faster than the read-erase-modify-write process needed for randomly written data going through garbage collection. The maximum speed will depend upon the number of parallel flash channels connected to the SSD controller, the efficiency of the firmware, and the speed of the flash memory in writing to a page.
Once the blocks are all written once, garbage collection will begin and the performance will be gated by the speed and efficiency of that process.
This requires even more time to write the data from the host. Please update this article to reflect recent events or newly available information. This will initially restore its performance to the highest possible level and the best lowest number possible write amplification, but as soon as the drive starts garbage collecting again the performance and write amplification will start returning to the former levels.
Unfortunately, the process to evenly distribute writes requires data previously written and not changing cold data to be moved, so that data which are changing more frequently hot data can be written into those blocks. The process requires the SSD controller to separate the LBAs with data which is constantly changing and requiring rewriting dynamic data from the LBAs with data which rarely changes and does not require any rewrites static data.
The reason is as the data is written, the entire block is filled sequentially with data related to the same file. Any garbage collection of data that would not have otherwise required moving will increase write amplification.
Wear leveling If a particular block was programmed and erased repeatedly without writing to any other blocks, that block would wear out before all the other blocks — thereby prematurely ending the life of the SSD.
An SSD with a low write amplification will not need to write as much data and can therefore be finished writing sooner than a drive with a high write amplification. Writing to a flash memory device takes longer than reading from it. With an SSD without integrated encryption, this command will put the drive back to its original out-of-box state.
In a perfect scenario, this would enable every block to be written to its maximum life so they all fail at the same time.
Each time data are relocated without being changed by the host system, this increases the write amplification and thus reduces the life of the flash memory. The benefit would be realized only after each run of that utility by the user. They simply zeroize and generate a new random encryption key each time a secure erase is done.
If the user or operating system erases a file not just remove parts of itthe file will typically be marked for deletion, but the actual contents on the disk are never actually erased.
Therefore, separating the data will enable static data to stay at rest and if it never gets rewritten it will have the lowest possible write amplification for that data.
If the user saves data consuming only half of the total user capacity of the drive, the other half of the user capacity will look like additional over-provisioning as long as the TRIM command is supported in the system. During this phase the write amplification will be the best it can ever be for random writes and will be approaching one.
The result is the SSD will have more free space enabling lower write amplification and higher performance.
The portion of the user capacity which is free from user data either already TRIMed or never written in the first place will look the same as over-provisioning space until the user saves new data to the SSD.
Write amplification in this phase will increase to the highest levels the drive will experience. The user could set up that utility to run periodically in the background as an automatically scheduled task.
One free tool that is commonly referenced in the industry is called HDDerase.Optimizing Space Amplification in RocksDB. CIDR By: Siying Dong, Mark Callaghan, Leonidas Galanis, Dhruba Borthakur, Tony Savor, Michael Stumm In particular, we optimize space efficiency while ensuring read and write latencies meet service-level requirements for the intended workloads.
This choice is motivated by the.
• Open sourced RocksDB • Big success within Facebook • Write amplification = 5 * num_levels • Increase memtable and level 1 size • Stronger (zlib, zstd) compression for bottom levels • Try universal compaction Next steps.
Next steps. Write amplification (WA) is an undesirable phenomenon associated with flash memory and solid-state drives (SSDs) where the actual amount of information physically written to the storage media is a multiple of the logical amount intended to be written.
Tuning RocksDB is often a trade off between three amplification factors: write amplification, read amplification and space amplification.
Write amplification is the ratio of bytes written to storage versus bytes written to the database. While InnoDB provides great performance and reliability for a variety of workloads, it has inefficiencies on space and write amplification when used with flash storage.
A few years ago, we built RocksDB, an embeddable, persistent key-value store for fast storage that has several advantages compared with InnoDB for space efficiency. Write amplification When we estimate write amplification, we usually simplify the problem by assuming keys are uniformly distributed inside each level.
In reality, it is not the case, even if user updates are uniformly distributed across the whole key range.Download