What is the difference between indexing and sharding. What is the role of both?
By default, 5 primary shards are created per index. These 5 shards can easily fit 100-250GB of data.
Sharding and partitioning are both about breaking up a large data set into smaller subsets. The difference is that sharding implies the data is spread across multiple computers while partitioning does not. Partitioning is about grouping subsets of data within a single database instance.
Database sharding splits a single dataset into partitions or shards. Each shard contains unique rows of information that you can store separately across multiple computers, called nodes. All shards run on separate nodes but share the original database's schema or design.
Hashed index: To support hash-based sharding, MongoDB supports hashed indexes. In this approach, indexes store the hash value and query, and the select operation checks the hashed indexes. Hashed indexes can support only equality-based operations.
Indexing is a way to store column values in a datastructure aimed at fast searching. This speeds up a search tremendously compared to a full table scan since not all rows will have to be examined. You should consider having indices on the columns in your WHERE clauses.
Sharding is a technique to split the table up between different machines. This makes it possible for parallell resolution of queries. For example, half the table can be searched on one machine and the other half on another machine. This will in some cases make it possible to increase the performance by adding more hardware, especially for large tables.
Indexing is the process of storing the column values in a datastructure like B-Tree or Hashing. It makes the search or join query faster than without index as looking for the values take less time. Sharding is to split a single table in multiple machine. For both indexing and searching it is necessary to select appropriate key.
For large tables, you should consider both indexing and sharding. For example, consider a Table X which has 1 million rows. If you search for a key K in table X, query processing will jump directly to row R which contains the key and return R to the user. If you do not cross your storage limit in most cases you don't need to shard a table. If you cross your storage limit you have to shard. There is no benefit sharding a small table as it will cause additional overhead of Network and aggregating subquery.
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