Deep Dive: CouchDB Storage Engine and B-Tree Fundamentals Quiz

Explore key concepts of CouchDB internals with this quiz focusing on the storage engine architecture and B-tree data structure implementation. Enhance your understanding of how data is stored, accessed, and managed efficiently in CouchDB using these core mechanisms.

1. B-Tree Structure Components

   Which structural element is essential for a B-tree node in the CouchDB storage engine, allowing efficient key searching?

   1. Linked list tails
   2. Pointers to child nodes
   3. Stacks of leaf records
   4. String concatenation arrays

   Explanation: Pointers to child nodes are a fundamental element of B-trees, enabling the tree to branch and facilitating fast searching. Stacks of leaf records are not how tree data is stored. String concatenation arrays and linked list tails are unrelated to the branching and searching capabilities required in B-trees.

2. Role of MVCC

   In CouchDB's storage engine, what is the primary benefit of using Multiversion Concurrency Control (MVCC) when updating documents?

   1. Compressing document attachments
   2. Reducing disk space for every write
   3. Speeding up schema migrations automatically
   4. Avoiding data locks during reads

   Explanation: MVCC allows reads and writes to occur without locking the entire database, enabling uninterrupted access to previous versions during updates. Reducing disk space is not the main benefit, as MVCC can actually increase storage requirements. Automatic schema migrations and attachment compression are unrelated to MVCC's core function.

3. Branch Factor in B-Trees

   What does the branching factor of a B-tree used in CouchDB primarily determine?

   1. The replication frequency
   2. The HTTP response time
   3. The maximum number of children a node can have
   4. The size of query result sets

   Explanation: The branching factor directly controls how many child nodes each B-tree node can reference, affecting the tree's depth and performance. While it influences query efficiency, it doesn't set result set size, replication timing, or HTTP response directly.

4. Document Update Behavior

   When a document is updated in CouchDB’s storage engine, how is data written according to its append-only design?

   1. Old data is overwritten in place
   2. A new record version is appended without altering the original
   3. All previous records are erased
   4. The update is stored only in memory

   Explanation: CouchDB's append-only strategy means every change creates a new version at the end of the file, preserving older versions for safety and consistency. Overwriting in place or erasing records contradicts the core philosophy, and storing updates only in memory would risk data loss.

5. Leaf Node Purpose

   Within a B-tree index in CouchDB, what primary data do the leaf nodes contain?

   1. Summary statistics for child nodes
   2. Actual key-value document references
   3. Replication checkpoint tokens
   4. Log files of write operations

   Explanation: Leaf nodes in B-trees hold the real data, storing references to actual documents' keys and values. They do not store child node summaries, log files, or replication checkpoint information, which are managed elsewhere.

6. Node Splitting Scenario

   What action does CouchDB’s B-tree implementation perform when inserting a key causes a node to exceed its capacity?

   1. Old keys are deleted from the node
   2. The node is compressed to reduce size
   3. The node is split into two nodes
   4. The insertion is skipped until the next cycle

   Explanation: When a node in a B-tree overflows during insertion, it is split, ensuring balanced structure and efficient search. Compressing, deleting keys, or deferring insertion are not standard behaviors in B-tree management.

7. Compaction Process

   In CouchDB, which process is responsible for reclaiming storage space by removing outdated document versions?

   1. Conflict resolution
   2. Database compaction
   3. Tree balancing
   4. Index mapping

   Explanation: Database compaction scans the database, identifies outdated versions, and removes them to save disk space. Tree balancing ensures index structure, while index mapping and conflict resolution do not directly reclaim storage.

8. Consistency with B-Trees

   In CouchDB, how do B-trees help maintain consistent view indexes after document updates?

   1. By freezing indexes during writes
   2. By recreating all indexes from scratch
   3. By storing additional cache copies
   4. By incrementally updating only affected tree nodes

   Explanation: CouchDB updates only parts of the B-tree relevant to the changed documents, avoiding the need to rebuild or freeze the entire index. Recreating from scratch or using additional caches is inefficient, and freezing would block important writes.

9. Write Amplification in CouchDB

   What potential drawback exists in CouchDB’s append-only storage design concerning frequent updates?

   1. Document retrieval always slows down
   2. Query results can become inconsistent
   3. Every write is instantly replicated globally
   4. Write amplification increases disk usage

   Explanation: Append-only updates can cause write amplification, leading to higher disk space consumption until compaction occurs. Immediate global replication, consistent query results, and slowdowns are not direct consequences of the append-only approach.

10. B-Tree Key Ordering

    Why is it important for key values stored in a CouchDB B-tree to be sorted?

    1. It enables efficient range queries and key lookups
    2. It ensures only one document can be stored per key
    3. It speeds up replication scheduling
    4. It prevents deletion of duplicate records

    Explanation: Sorted keys allow B-trees to quickly locate and traverse keys for searches and range queries. Sorting does not specifically address duplicate deletions, limit keys to one document, or affect replication scheduling directly.