This article covers the internal design of Durable Memorythe Ignite multi-tier storage architecture. Intended for Ignite developers.
Table of Contents
Let's split the memory into pages (synonyms: buffers, block, chunks). Let's consider the Page as a fundamental unit the whole memory is split into. This makes the memory addressing page-based.
Next, sometimes a query might require an SQL index for execution. The memory has to store not only data entries inside of the pages but builds and maintain maintains indexes as well. If Ignite used a memory-mapped files approach, it would be required to read all the data to process the first query. Instead of this, index data is also kept in pages and can store both in RAM on disk if the latter is enabled.
Now let's introduce an integer number that will define an index of Page - idx (4 bytes, unique within cache, partition and in current local node). In continious continuous memory segment page is located at specific offset:
Let's also add partition identifier (2 bytes), and composed indetifier identifier is effectivePageId, see PageIdUtils#effectivePageId
B+Tree are used for SQL indexes: tree maps field value to reference to entry value.
Page content is stored in RAM in segments. Actually memory region allocated in RAM is not continious sequence of pages.
In case of hash collision inside LoadedPagesTable lineral probe is used to find page by ID. If empty bucket is found, search is finished: page is not present in RAM.
Segment lock is read-write:
In case of flusing dirty page (see section page replacement below) there is additional synchronized map of pages being written to disk, so page read can't be started for such pages until removal from this map. This map is usually not big and most likely page is not there.
This section describes possible pages and entries operations related to rotation with disk or completely removal data from grid.
|Term||Activated||Comments||Configuration||Level of operation||In memory only mode||Persistency mode|
Expiration (aka TTL)
Sets expire time of entry after entry creation/access/update
/ a number of issues exist
Region is full
Completely removes entry from grid. Reasonable with 3rd party persistence
Entry (+ page is used to find more entries to remove)
On Heap eviction
Depends on policy
Near caches and On-Heap caches (1.x)
only for near /on-heap caches
Region is full
Not configurable by user
If durable memory operates with disk (native peristence you enable Ignite native persistence (that is covered in Ignite Persistent Store - under the hood), then the paging is still handled automatically by Ignite.
Page replacement may have negative influence to performance because in some cases it is possible that Ignite continiously evicts page from RAM and some steps later reqiures this page data. In this case it is required to re-read data from disc.
If Native Peristence is not used, then upcoming hit to memory limit requires Ignite to clean up some data (otherwise IgniteOutOfMemory may occur). In this case, users have to decide how to deal with out-of-memory situations by specifying their own eviction policy. Ignite will not magically page to disk and so users need to configure eviction.
If there are no concurrent updates, the page becomes empty and will be reused for other user data.
There is second option for eviction if Persitent Data store is not enabled. In that algorithm two most recent access timestamps are stored for every data page.
This policy solves case of one-time access of data, for example, one full scan query. Pages touched during running this query is not considered hot. See also documentation
Ignite manages free lists to solve issue with fragmentation in pages (not full pages).
If object is longer than page size, it will require several pages to store
In previous case (updated field value has same length) only one page will be marked as dirty.
There is class PageIO - it is abstract class for reading and writing pages. Several implementations also exist: BplusIO, DataPageIO
Link allows to read K,V pair as N-th item in page.
Deletion of latest added item is trivial. We can just remove Item, and Key-Value pair without any additional changes (see It3, K,V3 at picture).
Example of re-insertion new K,V4 element after some other K,V2 deleted. Following picture shows Indirect Item to direct Item replacement
B+Tree structure is build mostly the same as binary tree. If requied value was not found, it is compared with some value in the tree. Greater values can be found using rigth link, less - using left.
Hash Index is also B+ Tree (not hash table), key is hashcode and value is link.
DataRegionConfiguration (historical naming is Memory Policy) is especially important when Ignite Persistent Store configuration is enabled.
Reference tables (dictionaries) are usually small, and may be assigned to be allocated to memory always.
Following The following paragraph summarizes the results of memory structure changes
Ignite node with persistent store enabled may now start to operate without reading all pages data from disk.