Connection Pooling Explained: Sizing, Timeouts, and Database Overload
Connection Pooling Explained for teams who see timeouts when load spikes hard. Learn pool size, wait limits, and…
Category
System Design
These system design guides cover how to build software that scales and stays up. There are over a hundred posts, grouped by topic.
For a start, read load balancing and caching. Then high availability covers uptime. Also, each sub section goes deeper into one area.
112 articles
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The Saga Pattern: Distributed Transactions Without Two-Phase Commit
The saga pattern explained: long-lived business transactions as local steps with compensating transactions, orchestration vs choreography, the transactional…
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Distributed Locks: Mutual Exclusion Across Machines
Distributed locks explained: leases and TTLs as mutual exclusion across machines; the Redis distributed lock and its failover…
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Fault-Tolerant Systems: Redundancy, Failover, and Failure Detection
Fault tolerant system design: failure models from crash to byzantine, redundancy and the N+1 arithmetic, the failover playbook,…
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Distributed Transactions: Two-Phase Commit and Its Alternatives
Distributed transactions explained: two-phase commit step by step, the 2pc coordinator failure that leaves participants in doubt, consensus-backed…
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Leader Election in Distributed Systems: Methods and Split-Brain Risk
Leader election in distributed systems explained: leader election algorithms from the bully algorithm to quorum voting, zookeeper leader…
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Paxos Explained: The Algorithm Behind Most Consensus Systems
The Paxos algorithm explained: the paxos roles (proposers, acceptors, and learners) how prepare and accept ballots run, the…
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Raft Explained: Leader Election, Log Replication, and Safety
The Raft consensus algorithm explained: raft leader election with randomized timeouts, raft log replication through a strong leader,…
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Consensus Algorithms Compared: Paxos, Raft, ZAB, and PBFT
Consensus algorithms compared: Paxos and Multi-Paxos, the raft vs paxos difference, ZAB inside ZooKeeper, and byzantine fault tolerant…
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Distributed Consensus: Why Agreement Is Hard
Distributed consensus explained: the consensus problem, why agreement is hard under partial failure, the quorum math behind majority…
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CAP Theorem in Distributed Systems
Distributed systems are the backbone of modern technology, enabling everything from social media platforms to financial services. Yet,…
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Consistent Hashing: How Distributed Systems Place Data
Consistent hashing explained: the hash ring, virtual nodes, and the rendezvous hashing alternative; the placement scheme that lets…
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OAuth 2.0 and OIDC for Backend Engineers: Flows, Tokens, and Common Mistakes
OAuth 2.0 and OIDC explained for backend services. Learn code flow with PKCE, client credentials, token checks, and…
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What Is a Distributed System? Partial Failure, Clocks, and the Fallacies
What makes a system distributed: several machines cooperating over a network, partial failure as the defining property, no…
