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Paginated Local storage engine, also called as "plocal", is intended to be used as durable replacement of the previous local storage.
plocal storage is based on principle that using disk cache which contains disk data that are split by fixed size portions (pages) and write ahead logging approach (when changes in page are logged first in so called durable storage) we can achieve following characteristics:
- Operations on single page are atomic.
- Changes applied to the page can be restored after server crash even if they were not flushed to the disk.
Using write ahead log and page based cache we can achieve durability/performance trade off. We do not need to flush every page to the disk so we will avoid costly random I/O operations as much as possible and still can achieve durability using much cheaper append only I/O operations.
From all given above we can conclude one more advantage of plocal against local - it has much faster transactions implementation. In order achieve durability on local storage we should set tx.commit.synch property to true (perform synchronization of disk cache on each transaction commit) which of course makes create/update/delete operations inside transaction pretty slow.
Lets go deeper in implementation of both storages.
Local storage uses MMAP implementation and it means that caching of read and write operations can not be controlled, plocal from other side uses two types of caches read cache and write cache (the last is under implementation yet and not included in current implementation).
The decision to split responsibilities between 2 caches is based on the fact that characters of distribution of "read" and "write" data are different and they should be processed separately.
We replaced MMAP by our own cache solution because we needed low level integration with cache life cycle to provide fast and durable integration between WAL and disk cache. Also we expect that when cache implementation will be finished issues like orientechnologies/orientdb#1202 and orientechnologies/orientdb#1339 will be fixed automatically.
Despite of the fact that write cache is still not finished it does not mean that plocal storage is not fully functional. You can use plocal storage and can notice that after server crash it will restore itself.
But it has some limitations right now, mostly related to WAL implementation. When storage is crashed it finds last data check point and restores data from this checkpoint by reading operations log from WAL.
There are two kind of check points full check point and fuzzy check point. The full check point is simple disk cache flush it is performed when cluster is added to storage or cluster attributes are changed, also this check point is performed during storage close.
Fuzzy checkpoint is completely different (it is under implementation yet). During this checkpoint we do not flush disk cache we just store the position of last operation in write ahead log which is for sure flushed to the disk. When we restore data after crash we find this position in WAL and restore all operations from it. Fuzzy check points are much faster and will be performed each hour.
To achieve this trick we should have special write cache which will guarantee that we will not restore data from the begging of database creation during restore from fuzzy checkpoint and will not have performance degradation during write operations. This cache is under implementation.
So right now when we restore data we need to restore data since last DB open operation. It is quite long procedure and require quite space for WAL.
When fuzzy check points will be implemented we will cut unneeded part of WAL during fuzzy check point which will allow us to keep WAL quite small.
We plan to finish fuzzy checkpoints during a month.
But whether we use fuzzy checkpoints or not we can not append to the WAL forever. WAL is split by segments, when WAL size is exceed maximum allowed size the oldest WAL segment will be deleted and new empty one will be created.
The segments size are controlled by storage.wal.maxSegmentSize parameter in megabytes. The maximum WAL size is set by property storage.wal.maxSize parameter in megabytes.
Maximum amount of size which is consumed by disk cache currently is set using two parameters: storage.diskCache.bufferSize - Maximum amount of memory consumed by disk cache in megabytes. storage.diskCache.writeQueueLength - Currently pages are nor flushed on the disk at the same time when disk cache size exceeds, they placed to write queue and when write queue will be full it is flushed. This approach minimize disk head movements but it is temporary solution and will be removed at final version of plocal storage. This parameter is measured in megabytes.
During update the previous record deleted and content of new record is placed instead of old record at the same place. If content of new record does not fit in place occupied by old record, record is split on two parts first is written on old record's place and the second is placed on new or existing page. Placing of part of the record on new page requires to log in WAL not only new but previous data are hold in both pages which requires much more space. To prevent such situation cluster in plocal storage has following attributes:
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RECORD_GROW_FACTOR the factor which shows how many space will be consumed by record during initial creation. If record size is 100 bytes and RECORD_GROW_FACTOR is 2 record will consume 200 bytes. Additional 100 bytes will be reused when record will grow.
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RECORD_OVERFLOW_GROW_FACTOR the factor shows how many additional space will be added to the record when record size will exceed initial record size. If record consumed 200 bytes and additional 20 bytes will be needed and RECORD_OVERFLOW_GROW_FACTOR is 1.5 then record will consume 300 bytes after update. Additional 80 bytes will be used during next record updates.
Default value for both parameters are 1.2.
- USE_WAL if you prefer that some clusters will be faster but not durable you can set this parameter to false.