Write-Intensive Systems: Key Challenges in Distributed Systems

In System Design, Write-intensive systems are those where write operations dominate (e.g., IoT telemetry, logging, financial transactions, social media feeds). Scaling them introduces unique pressures that read-heavy systems don't face. But every design or architecture has its trade-offs, and designing write-intensive systems can present a lot of challenges that need to be addressed, depending on your business use case.

𝐃𝐢𝐬𝐤 𝐈𝐎 𝐚𝐧𝐝 𝐖𝐫𝐢𝐭𝐞 𝐀𝐦𝐩𝐥𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧

Writing directly to database disks on every request can create severe latency, especially if there are random writes. Every disk has its limited IOPS; even SSDs saturate under extreme write loads. The solution for this is to use techniques like LSM (Log Structured Merge Trees) that allow appending or writing data in sequential logs in memory and then flushing it into disk asynchronously. Databases like Apache Cassandra, Amazon DynamoDB, and RocksDB support this LSM data structure.

𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐠 𝐏𝐫𝐨𝐛𝐥𝐞𝐦

In highly scalable systems, we don't rely on a single node or server, but we have to have multiple nodes as backups where our system can read from, e.g., the Primary Node (main node for writing) and Secondary Nodes (backup nodes for reading). For some systems, we need to have our data consistent across all our nodes, and replicating the Primary Node changes into secondary nodes can take some time under heavy load, and replicas falling behind can cause stale reads and consistency issues. In that case, we choose a Sync or Async Replication strategy, or multi-leader replication, which will increase our writes but increase the risk of update conflicts.

𝐇𝐨𝐭 𝐒𝐩𝐨𝐭𝐬 / 𝐒𝐤𝐞𝐰𝐞𝐝 𝐖𝐫𝐢𝐭𝐞𝐬

During write-intensive workloads, when the system (database, servers, and partitions) experiences an unusually high load or demand leading to performance bottlenecks, reduced throughput, and even service failures, these problems are known as hot spots. This issue occurs when any system has disproportionate writes, such as a viral social media post or a trending stock ticker. To solve these issues, we need to have some sharding strategies or a consistent hashing technique to reduce the hotspot problem.

𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 𝐢𝐧 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐖𝐫𝐢𝐭𝐞𝐬 (𝐂𝐀𝐏 𝐓𝐡𝐞𝐨𝐫𝐞𝐦)

If we have a high-write-intensive system, chances are this will be distributed in nature. Under network partitions, we must choose between consistency and availability because writing to multiple replicas guarantees durability but requires a trade-off between them. In this case, we need to choose the replication strategy for either eventual or strong consistency that is more suited to our non-functional requirements.

𝐂𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲 𝐚𝐧𝐝 𝐋𝐨𝐜𝐤𝐢𝐧𝐠:

In write-intensive systems, there will be a lot of concurrent write requests hitting our system that can try to update the same record (e.g, an inventory record or a rate limit counter) in rows or partitions, which will cause lock contention. There are multiple solutions to handle this, depending on the use case; e.g., Multi Version Concurrency Control (MVCC) is a database technique that allows different transactions to read and update without blocking each other. Instead of overwriting a record when it is updated, the database creates a new version of that record. The user can read only the record while the new record writer updates a new version without stopping others. We can also use optimistic locking, which creates an actual database-level lock. This is also known as optimistic concurrency control (OCC).

𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠

Every write operation must update all secondary indexes. The more indexes, the higher the overhead and storage. Also single node db won't work when the data increases, and a single node will eventually run out of storage; thus, we need to distribute the data across different DB nodes, and as a result, index storage will also increase. The solution is to implement Horizontal Sharding by distributing data across multiple databases. This requires a carefully chosen partition key to ensure the write workload is evenly distributed across nodes, along with implementing write-optimized index structures.

Systems Worth Studying

Cassandra — LSM tree, tunable consistency, excellent write throughput
RocksDB — Embeddable LSM engine used by many write-heavy systems
Kafka — Sequential log writes, used as a write buffer in many architectures
ScyllaDB — Cassandra-compatible, designed to reduce write latency further
ClickHouse — Write-optimized columnar store for analytics

🔗 Let’s Connect!
If you enjoyed this post or would like to connect professionally, feel free to reach out to me on LinkedIn