Commonly asked Cassandra famous interview questions and answers.

1. What is a write ahead log in Cassandra?
   • In Cassandra, a write ahead log (WAL) is a log that is used to record all write operations that are performed on a node.
   • The WAL is used to provide durability guarantees by ensuring that all write operations are recorded before they are acknowledged as successful.
   • In the event of a node failure, the WAL can be used to recover any data that was in the process of being written at the time of the failure.
2. What is the purpose of a seed node in Cassandra?
   • In Cassandra, a seed node is a node that is used to bootstrap new nodes into the cluster.
   • When a new node joins the cluster, it contacts one or more seed nodes to obtain information about the topology of the network and to learn about other nodes in the cluster.
   • Seed nodes are typically configured to be the most stable and reliable nodes in the cluster.
3. What is a column family in Cassandra?
   • In Cassandra, a column family is a container for a set of related data that is organized into rows and columns.
     • Each row in a column family is identified by a unique key, and each column within a row represents a specific attribute of the data.
     • Column families are the basic unit of organization in Cassandra, and they are used to group related data together to enable efficient querying and retrieval.
   • In earlier versions of Cassandra, a column family was the basic unit of storage, similar to a table in a traditional RDBMS.
     • In more recent versions of Cassandra, column families have been replaced with tables, but the term is still sometimes used.
     • A column family in Cassandra is a collection of rows that share a common schema, similar to a table in a traditional RDBMS.
     • Each row in a column family can have a different number of columns, and columns can be dynamically added or removed as needed.
4. What is a compaction in Cassandra?
   • In Cassandra, compaction is the process of merging multiple SSTables together in order to remove duplicate data and to optimize storage and read performance.
     • Compaction is necessary in Cassandra because SSTables are immutable, which means that over time, multiple versions of the same data can accumulate on disk.
     • Compaction is used to eliminate these duplicates and to merge the contents of multiple SSTables into a single, more efficient file.
   • A compaction in Cassandra is a process that is used to merge SSTables (sorted string tables) together and to remove deleted or expired data.
     • Compaction is necessary in Cassandra because data is stored in SSTables, which can become fragmented over time as new data is added and old data is removed.
     • Compaction helps to improve query performance and reduce storage space by consolidating data and removing obsolete information.

5. What is the difference between a replica and a node in Cassandra?
   • In Cassandra, a node is a single instance of the Cassandra database running on a physical or virtual machine, while a replica is a copy of a piece of data that is stored on a different node in the cluster.
   • Replicas are used to provide fault tolerance and high availability, as they allow for data to be replicated across multiple nodes in the cluster.
6. What is the consistency level in Cassandra?
   • In Cassandra, the consistency level is a setting that determines how many replicas must acknowledge a read or write operation in order for the operation to be considered successful.
   • The consistency level can be set on a per-operation basis and can be used to trade off between read and write performance and consistency guarantees.
7. What is the purpose of a compaction strategy in Cassandra?
   • In Cassandra, a compaction strategy is a configuration setting that determines how SSTables are merged and compacted together.
   • Different compaction strategies can be used to optimize read performance, write performance, or disk space utilization, depending on the needs of the application.
8. What is a rack in Cassandra?
   • In Cassandra, a rack is a logical grouping of nodes within a data center that are physically co-located and typically connected by a high-speed, low-latency network.
   • Racks are used to enable fault tolerance by ensuring that replicas of data are stored on nodes that are physically separate from each other.
   • By storing replicas on different racks, the impact of network outages or power failures can be minimized.
9. What is a clustering column in Cassandra?
   • In Cassandra, a clustering column is part of a primary key that is used to determine the order in which data is stored within a partition.
   • Clustering columns are used to support efficient range queries and sorting of data within a partition.
10. What is a slice in Cassandra?
    • In Cassandra, a slice is a subset of columns or rows from a column family that is returned as a result of a read operation.
    • Slices can be defined using range queries or by specifying a list of column names.
11. What is a batch statement in Cassandra?
    • In Cassandra, a batch statement is a mechanism that is used to group multiple read or write operations into a single atomic operation.
      • Batch statements can be used to improve write performance by reducing the overhead of sending multiple separate write operations to the database.
      • Batch statements can also be used to ensure consistency when performing multiple write operations that are related to each other.
    • A batch statement in Cassandra is a collection of CQL statements that are executed as a single atomic operation.
      • Batch statements can include insert, update, and delete statements, and can span multiple tables.
      • Batch statements are used to ensure atomicity and consistency of updates, and can help to improve write performance by reducing the number of round-trips to the database.

12. What is the purpose of a consistency level in Cassandra?
    • In Cassandra, a consistency level is used to determine how many replicas of a piece of data must acknowledge a read or write operation before the operation is considered successful.
    • Consistency levels are used to balance the trade-offs between consistency, availability, and partition tolerance in a distributed database.
13. What is the difference between eventual consistency and strong consistency in Cassandra?
    • In Cassandra, eventual consistency is a consistency model in which different replicas of a piece of data may diverge temporarily, but will eventually converge as updates propagate through the system.
    • Eventual consistency is used to ensure high availability and partition tolerance in a distributed system, but may result in temporary inconsistencies in the data.
    • Strong consistency, on the other hand, is a consistency model in which all replicas of a piece of data must be in sync before a read or write operation is considered successful.
    • Strong consistency ensures that data is always consistent, but may result in reduced availability and increased latency due to the need for coordination between replicas.
14. What is a token in Cassandra?
    • In Cassandra, a token is a numeric value that is assigned to each node in the cluster based on the hash value of the node’s partition key.
    • Tokens are used to determine which node in the cluster is responsible for storing and serving a particular piece of data.
    • The token range of each node is used to determine which replicas of a piece of data must be queried or updated to ensure consistency.
    • In Cassandra, a token is a value that is used to determine which node in a cluster is responsible for storing a particular piece of data.
    • Tokens are assigned to nodes in the cluster based on a hash of the node’s IP address, and are used to partition the data across the cluster.
    • Each token corresponds to a range of keys, and the node responsible for a particular token is responsible for storing all of the data within that range.
15. What is compaction in Cassandra?
    • In Cassandra, compaction is the process of merging and removing obsolete data from SSTables to reduce disk space usage and improve query performance.
      • Compaction is performed automatically by Cassandra based on a configurable set of criteria, such as the number of SSTables or the amount of disk space used.
    • Compaction in Cassandra is a process that is used to merge multiple SSTables into a single, larger SSTable.
      • Compaction is important in Cassandra because it allows data to be efficiently stored and queried, even as the amount of data in the cluster grows.
      • Compaction can be performed automatically by Cassandra based on configurable settings, or can be performed manually using the nodetool utility.

16. What is a commit log in Cassandra?
    • In Cassandra, a commit log is a durable, sequential log of all write operations that are performed on the database.
    • The commit log is used to ensure durability of write operations in the event of a node failure or system crash.
    • A commit log in Cassandra is an append-only log that is used to ensure data durability and consistency.
    • When a write operation is received by Cassandra, the data is first written to the commit log on disk.
    • Once the data has been written to the commit log, it is considered durable, meaning that it will not be lost even if the node fails before the data is written to an SSTable.
    • The data is then written to the memtable for the corresponding table, and eventually flushed to disk as an SSTable.
    • The commit log is used to ensure that data is not lost
17. What is hinted handoff in Cassandra?
    • In Cassandra, hinted handoff is a mechanism that is used to temporarily store write operations on nodes that are temporarily unavailable.
    • When a write operation is sent to a node that is unavailable, the coordinator node responsible for the write will create a hint that is stored locally.
    • When the unavailable node comes back online, the hint will be delivered and the write operation will be performed.
    • Hinted handoff is used to ensure that data is not lost due to temporary network or node failures.
18. What is a counter column in Cassandra?
    • In Cassandra, a counter column is a special type of column that is used to store a counter value that can be incremented or decremented atomically.
    • Counter columns are used to support distributed counters, such as the number of page views for a website.
    • In Cassandra, a counter column is a column that is used to store a counter value that is incremented or decremented atomically across the cluster.
    • Counter columns are typically used to store aggregate values that are updated frequently, such as page views or likes on a social media site.
19. What is a secondary index in Cassandra?
    • In Cassandra, a secondary index is an index that is created on a non-primary key column in a table to allow efficient querying of data based on that column.
    • Secondary indexes are not as efficient as primary key indexes, as they require additional disk reads and network traffic to retrieve the data.
    • A secondary index in Cassandra is an index that is created on a non-primary key column in a table.
      • Secondary indexes can be used to support efficient queries on columns that are not part of the primary key, but are typically not as performant as queries that use the primary key.
      • Cassandra supports two types of secondary indexes: SASI (SSTable Attached Secondary Index) and standard secondary indexes.
      • SASI indexes are optimized for range queries on large data sets and are suitable for use on columns with high cardinality, while standard secondary indexes are better suited for small data sets or queries that involve equality filters.
      • Secondary indexes are created using the CREATE INDEX statement in CQL.
    • In Cassandra, a secondary index is an index that is created on a non-primary key column in a table.
      • Secondary indexes allow for efficient querying of data based on attributes other than the primary key, and they are typically used to support use cases where data is frequently queried based on a specific attribute.
      • However, secondary indexes can also impact write performance and can lead to increased storage requirements, so they should be used judiciously.
20. What is a materialized view in Cassandra?
    • In Cassandra , a materialized view is a pre-computed view of a table that is optimized for a specific query pattern.
      • Materialized views are created by defining a new table that is populated with data from the original table using a SELECT statement with a WHERE clause that defines the view’s query pattern.
      • Materialized views can improve query performance by reducing the amount of data that needs to be scanned and aggregated at query time.
    • A materialized view in Cassandra is a pre-computed view of a table that is stored separately from the base table.
      • Materialized views are created using the CREATE MATERIALIZED VIEW statement, and are updated automatically whenever the base table is updated.
      • Materialized views can be used to improve query performance by reducing the amount of data that needs to be read from the base table.
      • Materialized views are especially useful for queries that involve filtering on non-primary key columns, since secondary indexes can be slow and inefficient.
    • In Cassandra, a materialized view is a denormalized view of a table that is optimized for a specific query pattern.
      • Materialized views are created by defining a new table that is derived from an existing table and specifying a primary key and clustering columns that are optimized for the desired query pattern.
      • Materialized views can improve query performance by precomputing and storing results that would otherwise need to be computed at query time
21. What is a tombstone in Cassandra?
    • In Cassandra, a tombstone is a special marker that is used to indicate the deletion of a row or column.
      • Tombstones are used to ensure eventual consistency by ensuring that deleted data is not resurrected from old replicas. Tombstones are also used to prevent stale data from being returned in read operations, as a tombstone will cause a read operation to return null or an empty value.
    • A tombstone in Cassandra is a marker that is used to indicate that a particular row or column of data has been deleted.
      • When data is deleted in Cassandra, a tombstone is written to indicate that the data should no longer be included in read operations.
      • Tombstones are necessary in Cassandra to ensure data consistency across replicas, as they allow deleted data to be replicated to other nodes in the cluster.
      • However, if tombstones are not properly managed, they can lead to performance problems and increased disk usage.

22. What is a snitch in Cassandra?
    • In Cassandra, a snitch is a component that is responsible for determining the topology of the cluster and mapping nodes to racks and data centers.
    • Snitches are used to ensure that data is stored and replicated across multiple data centers and racks to ensure high availability and fault tolerance.
    • A snitch in Cassandra is a component that is responsible for determining the topology of the cluster, including which nodes are in which data centers and racks.
    • The snitch is used to route read and write operations to the appropriate nodes in the cluster based on their proximity and availability.
    • Cassandra includes several built-in snitches, such as the SimpleSnitch, which is suitable for single-data center clusters, and the GossipingPropertyFileSnitch, which is suitable for multi-data center clusters. Custom snitches can also be developed to support different network topologies.
23. What is a hinted handoff timeout in Cassandra?
    • In Cassandra, a hinted handoff timeout is the amount of time that a coordinator node will wait before discarding a hint for a write operation that has not been performed.
    • Hinted handoff timeouts are used to prevent hints from accumulating indefinitely and causing performance issues in the cluster.
24. What is a gossip protocol in Cassandra?
    • In Cassandra, a gossip protocol is a protocol that is used by nodes in the cluster to exchange information about the state of the cluster, such as the location of nodes and the status of data.
    • The gossip protocol is used to ensure that nodes in the cluster are aware of each other and that data is replicated correctly.
    • The gossip protocol in Cassandra is a protocol that is used by nodes in a cluster to discover and communicate with each other.
    • The gossip protocol is used to maintain a shared view of the state of the cluster, including information about the nodes, the data that is stored on each node, and the state of each node.
    • The gossip protocol is designed to be lightweight and efficient, allowing nodes to quickly and easily discover and communicate with each other.
25. What is a hinted handoff coordinator in Cassandra?
    • In Cassandra, a hinted handoff coordinator is the node that is responsible for coordinating hinted handoff operations in the cluster.
    • The hinted handoff coordinator is responsible for creating and delivering hints to nodes that are temporarily unavailable, and for managing hinted handoff timeouts to prevent hints from accumulating indefinitely.
26. What is a Bloom filter in Cassandra?
    • In Cassandra, a Bloom filter is a probabilistic data structure that is used to test whether a particular piece of data may exist in a set.
    • Bloom filters are used in Cassandra to reduce the number of disk reads required to perform a query by allowing Cassandra to quickly eliminate SSTables that do not contain the queried data.
    • A Bloom filter is a data structure that is used to quickly check whether an item is a member of a set.
    • In Cassandra, Bloom filters are used to determine whether a given row exists in an SSTable, without having to read the entire SSTable into memory.
    • This can greatly reduce the amount of I/O required to perform queries, and can improve query performance.
    • Bloom filters are stored on disk alongside SSTables, and are loaded into memory when the SSTable is opened.
27. What is a consistency level override in Cassandra?
    • In Cassandra, a consistency level override is a mechanism that is used to override the default consistency level for a specific read or write operation.
    • Consistency level overrides are used to ensure that critical operations, such as updates to sensitive data, are performed with a higher level of consistency than the default level.