Easy everything in Backend Dev, page 3
Inverted index in search engines
An inverted index maps each term to the list of documents containing it, making keyword lookup O(1)-ish instead of scanning every document.
Schema-on-read in data lakes
Structure is applied at query time not ingest, enabling flexible raw storage and ML, but costing query-time validation and risking data swamps.
Data warehouse vs data lake
Warehouses store structured, schema-on-write data for BI; lakes store raw multi-format data with schema-on-read for exploration and ML.
Replica lag and read-your-writes consistency
Stale reads come from async replica lag, the guarantee a user expects is read-your-writes, and you route that user's reads to the primary after a write.
Read replica vs Multi-AZ in RDS
Multi-AZ is synchronous standby for failover, read replicas are async copies for read throughput, and the two solve different problems.
Managed RDS vs self-managed DB on EC2
Managed RDS offloads patching, backups, failover, and replication, freeing the team to build product; self-managed EC2 means you own all that toil.
Connection pools and the problem they solve
A pool reuses pre-opened connections so requests skip the expensive connect handshake; without one, every request pays setup latency and may overwhelm the database.
Purpose of database drivers (JDBC/ODBC)
A driver translates a standard API into each database's wire protocol, so app code stays portable across vendors.
Least privilege for database service accounts
Grant each account only the minimum rights its job needs; for an app service account, scope grants to specific tables and verbs, never use the superuser.
Full, differential, and incremental backups
Full copies everything; differential copies all changes since the last full; incremental copies changes since the last backup of any type.
What is sharding and why shard over vertical scaling?
Sharding splits one dataset across servers by a shard key so each holds a subset; you shard because vertical scaling hits hardware ceilings, gets costly, and remains a single point of failure.
What is database replication and why use it?
Replication keeps copies of data on multiple servers; primary benefits are high availability through failover and improved read scalability by spreading reads.
What is the difference between ETL and ELT?
ETL transforms before loading, on a separate engine; ELT loads raw then transforms inside a scalable warehouse. Choose ELT with cloud warehouses and large raw or schema-on-read data.
What is a star schema?
A central fact table of measures and foreign keys surrounded by denormalized dimension tables of descriptive attributes, joined in one hop for fast, simple analytical queries.
What is the difference between OLTP and OLAP?
OLTP handles many short read-write transactions on normalized current data; OLAP runs few large analytical scans over denormalized historical data.
What is the CAP theorem?
Consistency, Availability, Partition tolerance; during a network partition you must choose between staying consistent or staying available.
How do you choose between relational and NoSQL databases?
Relational gives schema, joins, and ACID for structured related data; document gives flexible schema and horizontal scale for varied or denormalized data.
Joining a large table with a small one
With a tiny table the optimizer often picks a hash join, building a hash table on the small side in memory, then probing it once per row of the large table in a single pass.
Why developers read query plans
The plan shows the operators the optimizer chose to run a query; developers read it to find why a query is slow; a common thing to look for is a full table scan where an index was expected.
Stages of executing a SELECT query
Parse the SQL into a tree, bind and validate against the catalog, optimize into a physical plan, then execute the plan operators fetching data and returning rows.
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