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Let's describe the B+ Tree in more detail to understand the need for invoke operation.
The keys of the tree (hashes) are stored on the B+ Tree pages (index pages), the cache key-value itself is stored on data pages. Each item on the index page includes a link to the data page item. In general, a B+ Tree supports find, put and remove operations. For put andremove, you must first find the point of insertion/update/removal. So, cache entry update without invoke operation can look like this:
- Search B+ Tree for link to old key-value (find)
- The old value does not differ in length - a simple value update of key-value on the data page
- The old value differs in length - the link to it changes:
- Store a new key-value into data page
- Put B+ Tree key (with "secondary" find) to update link to data page item
- Remove old key-value from data page
The invoke operation uses an in-place update and has the following execution scheme:
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- Batch writing to data pages
- Batch updates in B+ Tree
Diagram overview
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Batch writing to data pages
Divide the input data rows into 2 lists:
- Objects whose size is equal to or greater than the size of a single data page.
- Other objects and remainders (heads) of large objects.
Sequentially write objects and fragments that occupy the whole page. The data page is taken from "reuse" bucket, if there is no page in reuse bucket - allocate a new one.
For remaining (regular) objects (including the remainders ("heads") of large objects) , find the most free page with enough space in FreeList (allocate new one if there is no such page) and fill it up to the end.
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Overall changes to support batch updates in PageMemory can be divided into following phases.
Phase 1:
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Batch insertion in FreeList to improve rebalancing
- Implement insertDataRows operation in FreeList - insert several data rows at once.
- Implement invokeAll operation in BPlusTree: support searching and inserting range of keys.
- Enable batch insertion in preloader (enabled by a special system property)Preloader should insert a batch of data rows before initializing cache entries. In the case when the cache entry is initialized incorrectly, preloader should rollback changes and remove pre-created data row.
Phase 2:
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DataStreamer support
- InvokeAll operation should support batch update of existing keys.
Phase 3: DataStreamer support
- Add support for batch updates in DataStreamer (inserts in FreeList in the isolated updater (similar to the preloader).
Phase
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3: putAll support
- Add support for Implement batch updates in IgniteCache putAll.
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- operations in B+ tree (findAll/putAll/removeALl/invokeAll).
Examine the performance difference between the following approaches and select the best:
A. single updates (current approach)
B. sort + BPlusTree.invokeAll() + FreeList.insertDataRow
C. sort + BPlusTree.findAll + FreeList.insertDataRows + BPlusTree.putAll
Phase 4: MVCC support
- Add support for MVCC (TRANSACTIONAL_SNAPSHOT) cache mode.
Risks and Assumptions
- Concurrent update of the cache entry is blocked until the batch operation with the same key is completed, therefore the batch size must be strictly limited.
- Duplicate keys processing (WAL rebalancing) based on assumption that duplicate keys will be ordered according to cache entry version (a newer version is the latest)For BPlusTree batch operations, ordered keys are required, moreover, an attempt to simultaneously lock the same keys in a different order lead to a deadlock, so batch insertion into the page memory must be performed on an unlocked entries. Alternatively, keys passed in batches from different components (preloader, datastreamer, putAll) should be locked in the same order.
- Heap usage/GC pressure.
Prototype testing results
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Parameters: 1 node, 1 cache, 1 partition, 500 100 objects, message size is not limited, 4k page.
Entry size (bytes) | 44-5656-104 | 140-240340 | 240340-540740 | 500740-8001240 | 8001240-12003040 | 2000-3000 |
| 10001040-800080404000- | 4040-16040 (fragmented) 16000 |
| 100-32000 (fragmented mostly) |
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Time improvement (%) | 4543.1445 | 37.21 | 4233.59 | 4128.6243. | 0 | 405.8468 | 10.41 | 708.6 |
| 691.91 | 64.2 |
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Testing on dedicated servers
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Jira |
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server | ASF JIRA |
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columns | key,summary,type,updated,assignee,reporter,priority,status,resolution |
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maximumIssues | 20 |
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jqlQuery | project = Ignite AND labels IN (iep-32) order by key |
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serverId | 5aa69414-a9e9-3523-82ec-879b028fb15b |
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The improvement in rebalancing time when using batch insertion is mostly noticeable when writing small objects and decreases with increasing object size.the improvement in total rebalancing time is reduced in cases of demander idle waiting for the next messages from the supplier