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1.Concepts
- Bucket
A container used to locate index records. Each element in the container records the unique number of a table record, and an empty element indicates that there is no table record at the current index location.
- Bucket Size
The number of unique record numbers that can be stored directly in a bucket.
- Bucket Subscript
The bucket subscript is obtained by index column modulo the Hash value and the bucket size after Hash calculation.
- Index Record Chaining Table
Due to the existence of Hash conflict, the bucket subscripts obtained from different table records after Hash calculation may be the same. The same bucket subscripts of index records are stored in a two-way chain table, that is, the index record chain table, the element on the bucket is the head of index record chain table.2.Memory Structure
AntDB-M's memory structure of Hash index is designed to be very simple and efficient. Each Hash index has two parts: bucket, index record chain table.
- Bucket
Bucket is a one-dimensional array, each element in the array is a unique number of table record, and the unique number of index record is the same as the unique number of table record.
Figure 1: Bucket
- Index Record Chaining Table
AntDB-M index record chain table is used to record index records with the same hash value. The memory structure is a three-level structure: 1) primary address; 2) secondary address; 3) chain table nodes; the organization of this structure is similar to that of table data (refer to "AntDB-M’s Memory Structure"), and the records in the chain table can be quickly located by the unique number of the records.
The nodes of the index record chain table have three parts: the previous record number, the next record number, and the transaction (only the unique index has it). The record number of the record in the bucket is the record number of the head node of the chain table.
Figure 2: Index record chain table
As you can see above, the entire structure of the Hash index involves only the record number and transaction information, which is a very lightweight structure that scales memory on demand and does not consume too much memory.3.Index Processing
3.1 Persistence
AntDB-M index includes two parts of data: index metadata, index records. When doing the persistence of data, only the index metadata will be persisted.
3.2 Initialization
When AntDB-M database service starts, first load the table data records, then load the index metadata, and finally initialize the index records in memory according to the index metadata.
3.3 Insert Index Records
When a table inserts a record, the table record will be inserted first, and then the index record will be inserted. The new record is added to the head of the index chain table to improve the insertion speed.
- General Index
Figure 3: insert general index
For general indexes, because there is no need to determine the uniqueness of records, concurrent inserts do not affect each other and can be inserted directly. The newly inserted index record is effective immediately, i.e., it can be accessed immediately when other transactions query it, but the visibility of the table record is controlled by the transaction information on the table record as well as the lock. On transaction commit, the general index does not require any further processing. The transaction removes the index record itself from the index record chain table when rolling back.
- Unique index
Figure 4: insert unique index
For unique indexes, it is necessary to detect the presence of a unique key conflict. When the same record already exists in the chain table of index records with the same bucket subscript and the transaction has not been committed (the transaction field on the index record has a value), it blocks and waits for other transactions to commit or roll back to detect the unique key conflict. If the transaction is committed, the transaction information on the index record is cleared. If the transaction is rolled back, the index record is deleted. Transaction commit and rollback both notify the transaction that is blocking and waiting.
3.4 Index Query
When querying by hash index, the hash bucket size is first modulo the hash value calculated by the index column to get the bucket subscript, then get the list of index records under the subscript, get the record number of the matching record, and put the number into the query context allocated for the current transaction for subsequent query traversal. The index record has the same record number as the data record, and the data record can be located immediately based on the record number. The main overhead of the whole process is the calculation of the hash value and the comparison of the indexed record items, and the performance is very high.
3.5 Deleting Index Records
Figure 5-General index deletion
Figure 6 - Unique Index Deletion
When a table deletes a record, it first deletes the table data record (marks the delete mark and updates the transaction information), and then updates the transaction on the unique index record to the current transaction. Since ordinary indexes do not have transaction information, no operation is done. The transaction on the updated index record is for conflict detection when the data record uniqueness is concurrent in multiple transactions. Since the current transaction has already acquired the mutex lock on the record, updating the index record does not affect the consistency of the index record and can be updated directly.
When the transaction commits, the index record is not immediately deleted, and the index record is still found by the index during lookup. The visibility of the table record is still controlled by the transaction information and lock information on the table record (i.e. MVCC). A separate cleanup thread will later perform the actual deletion of the data record and index record when the data record is no longer accessed by the transaction.
When the transaction is rolled back, only the transaction information on the index record needs to be cleared for unique indexes.
In the case of unique indexes, the transaction commit and rollback will notify the transaction that is blocking and waiting.
3.6 Update Index Entries
Updates to table records update the indexes only when index column updates are involved. When an index update is involved, the modification of table records is converted to deleting the old records first and inserting the new ones later. The operations for indexes are similarly converted to inserting index entries and deleting index entries as described above. The commit and rollback of transactions are also the same as inserting and deleting indexes as described above.
3.7 Node-Chain Table Extensions
The space size (number of records) of the index node chain table is the same as the space size of the table records. When the new record in the table exceeds the table space size and the table space needs to be extended, the index node chain table is extended at the same time.
3.8 Bucket reconstruction (rehash)
When creating an index, the initial value of the index bucket size can be specified by the index attribute block_size, or by the current number of table records if not specified, with a minimum value of 100000. If the number of records exceeds the bucket size, the bucket will be expanded and the hash index will be rebuilt.
- New Bucket Size
The new bucket size is the smallest prime number larger than the current bucket size.
- Bucket access lock
For each thread, the first access to the index is assigned a bucket access node with the current bucket address and a mutex lock. When a thread starts accessing the index, it first requests the lock on the access node and releases the lock at the end of the access. The lock ensures that the bucket resources are not released due to rehash during the index access.
- Asynchronous Rebuilding
Bucket rebuild is an asynchronous process, where the bucket rebuild process does not affect index queries or updates. The process affects whether the old bucket is accessed or the new bucket by recording the current migration location. When data migration is complete and there is no access to the old bucket (no thread holds a bucket access lock on the bucket), the old bucket resources are released.
3.9 Indexes and Locks4.Index Features
To avoid too many locks taking up too many system resources and to maintain high concurrency, each Hash index of AntDB-M database is assigned 131 locks, and the lock of the current index record is obtained by bucket subscript with 131 taking modulo. Locks are added first during accessing indexes (query or modification).
4.1 Prefixed Indexes
A prefix index is one that applies a specified column, or a specified prefix length of a specified column, as an index.
This characteristic is applicable in the following two cases:
1.Index column data is too long
2.The data of the index column part is applicable to the scenario of specific business query.
AntDB-M can also specify the prefix length of each column, not just the prefix length of the last column, which provides a very flexible indexing capability for business and facilitates customers to achieve more efficient and responsive business landing.
4.2 Data Distribution5.Restriction Constraints
For each Hash index, the number of records with the same Hash value under the current position is counted.
This statistic is mainly used to determine whether there is a large Hash conflict in the current data. It is convenient for the operation and maintenance managers of AntDB-M to quickly locate whether it is suitable to build a Hash index and whether it is necessary to further optimize the Hash index.
5.1 Do not support range query6.Conclusion
For Hash indexes, range queries are not supported because the indexed records themselves are not sorted.
Note: Range queries on indexed column fields are performed with full table traversal.
5.2 Fuzzy query is not supported
AntDB-M supports fixed-length left prefix matching. However, fuzzy matching is not supported because the Hash calculation itself requires exact values.
5.3 Index columns should not have too many identical values
If there are more columns with the same value, these records will be located in the same index record chain table, and each query will iterate through all the records in this list for filtering, the more data, the lower the performance.
It is the simplicity of Hash indexing that allows AntDB-M database to provide efficient indexing capability with less memory and computation, which improves database access performance while significantly reducing the system resource overhead. However, there are some limitations on the use of Hash indexing algorithm, mainly due to its own characteristics.
Understanding these characteristics can help business model designers to choose the right index type to maximize the performance of the database.
