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This article covers the internal design of Durable Memorythe Ignite multi-tier storage architecture. Intended for Ignite developers.

The reasoning behind the durable memory storage architecture is described in the main documentation:  https://apacheigniteignite.readmeapache.ioorg/docs/latest/durablememory-memoryarchitecture

Table of Contents



Multi-Tier Storage Architecture

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.

Region and segment structure

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 and Page Locks

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.

Eviction, rotation and expiration

This section describes possible pages and entries operations related to rotation with disk or completely removal data from grid.

TermActivatedCommentsConfigurationLevel of operationIn memory only modePersistency mode

Expiration (aka TTL)


Sets expire time of entry after entry creation/access/update

ExpiryPolicy (Factory)



(plus)/(minus) 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)



(plus) only for near /on-heap caches

Page replacement

Region is full

Ignite operates

Not configurable by user



(plus) Always enabled

Page replacement (rotation with disk)

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.

Entry Eviction

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

Entries and pages


Location of objects


Free lists

Ignite manages free lists to solve issue with fragmentation in pages (not full pages).


  • Consult marshaller about size in bytes of this value pair
  • Upper-round this value to be divisible by 8 bytes
  • Use value from previous step to get page list from free list
  • Select some page from appropriate list of free pages. This page will have required amount of free space

Long objects

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.

Page structure

Page types and headers

There is class PageIO - it is abstract class for reading and writing pages. Several implementations also exist: BplusIODataPageIO


Link allows to read K,V pair as N-th item in page.

Value delete from Data 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


BPlus Tree Structure

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.

Data Region Configuration

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.


Storage Architecture: Ignite 2.x vs Ignite 1.x

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.

See also

Persistent Store Architecture and Persistent Store Overview