Redis licensing changes have teams rethinking their in-memory data strategy. 🧠 Valkey offers the same performance without vendor lock-in or licensing surprises—making it the smart choice for organizations that want control over their infrastructure roadmap. Our guide breaks down the practical differences, migration paths, and long-term implications so you can make the right call for your stack. 👉 https://bit.ly/4n71bff
Rethink In-Memory Data Strategy with Valkey
More Relevant Posts
-
"We will share baseline performance data, including restore speeds exceeding 100 TB/hr, and provide practical guidance on integrating FlashBlade with native T-SQL backup using SMB and S3 protocols." #Database #TechTalks #PureStorage
To view or add a comment, sign in
-
The Idempotency-Key header is an anti-pattern for high-throughput systems. The standard advice is to use an `Idempotency-Key` header to prevent duplicate mutations. The client generates a UUID, sends it with a `POST`, and the server stores it to reject replays. Simple. But at scale, this pattern introduces a major bottleneck. Every write operation now requires a read-lock-write on a shared resource: the idempotency key table. It's effectively a distributed lock managed by your API clients, turning a scalable service into one that serializes requests based on a single key lookup. Your database becomes the choke point. We ran into this with an order processing service. Performance degraded as transaction volume grew because every `createOrder` call was contending for locks on the key store. The better approach is designing for business-level idempotency. Instead of a generic key, use domain-specific constraints. For the order service, we checked for an existing order from the same user with identical line items within a 5-minute window. This check could be against a fast cache like Redis, avoiding the primary database contention entirely. Design idempotency into your domain logic, not as a generic wrapper around your API. #SystemDesign #DistributedSystems #SoftwareArchitecture
To view or add a comment, sign in
-
-
Our customer TriZetto Provider Solutions handles healthcare data for millions of Americans every single day. Before MongoDB Atlas, their teams needed to focus on database maintenance like patching, tuning, and managing disaster recovery, instead of building innovative solutions. After migrating to Atlas, the results were transformative: 80% lower latency, 2x–3x faster performance, 80% fewer manual tasks, and $1.1 million in savings. These results freed up their team to drive further innovation rather than wasting time on DB overheard. Read the full story: https://lnkd.in/ea7xub-N
To view or add a comment, sign in
-
Indexes don’t make databases faster. The right indexes do. I still see production systems with indexes created “just in case.” In PostgreSQL, improper indexing can: – Increase write latency – Increase storage cost – Degrade overall performance Composite indexes work best when queries filter by multiple columns in the same order. Partial indexes shine when you only query a subset of rows (e.g., active users only). Real-world case: teams optimizing high-volume SaaS dashboards often report 40–60% query latency reductions after revisiting index strategy instead of scaling infrastructure. Before adding replicas, check your query plan. Run EXPLAIN ANALYZE. Performance is often architectural, not hardware. #PostgreSQL #DatabaseOptimization #BackendEngineering #PerformanceTuning
To view or add a comment, sign in
-
-
Scaling data collection is usually about distributing work. The answer is usually a queue, multiple workers, and a shared view of rate limits and database load. The design is similar whether we’re using Redis, RabbitMQ, or SQS. Here are some patterns used, put together into a short guide: when to scale out vs up, queue topology, rate limits across workers, and keeping the DB from becoming the limit. https://lnkd.in/dr2uJutD #DataEngineering #WebScraping #DistributedSystems #DataCollection
To view or add a comment, sign in
-
-
MXstore Australia strengthens business resilience with OCI Full Stack Disaster Recovery. Australia’s leading online motorcycle gear retailer is enhancing operational continuity using OCI Full Stack DR. By leveraging OCI Full Stack DR, MXstore orchestrates end-to-end disaster recovery across compute, database, and application tiers — including OCI Kubernetes Engine (OKE) and MySQL HeatWave — reducing recovery time, increasing automation, and ensuring business continuity for mission-critical workloads. See how MXstore is building a resilient, cloud-first architecture with Oracle. 👉 https://lnkd.in/g_73hZzp #OCIFullStackDR #DisasterRecovery #BusinessContinuity #OCI #CloudResilience #OracleCloud #CustomerSuccess Praveen Sampath Glen Hawkins Raphael Teixeira Gregory King Jason Dodunski
To view or add a comment, sign in
-
-
🚀 DuckDB now supports querying Snowflake! Key features include: Multiple authentication methods (Password, SSO, Key Pair) Direct SQL passthrough with snowflake_query() Attach Snowflake databases as DuckDB catalogs Predicate pushdown for optimized queries Hybrid queries: join Snowflake tables with local DuckDB tables Full DML reads: SELECT with WHERE, JOIN, aggregations, subqueries This can be super useful for developing in-memory caches — you can query smaller lakehouse/S3 tables directly in DuckDB, and larger tables can default to Snowflake seamlessly. 📌 https://lnkd.in/eX6w6gmf https://lnkd.in/e_fh95CX
To view or add a comment, sign in
-
Why I love OCI Most clouds attempt to be “helpful” by rebooting infrastructure in the background when a label is changed. Not OCI. OCI prioritizes Data Plane Integrity. When you change a tag, OCI protects your workload and waits for you to make the call. In this 60-second clip, I break down: - The “Metadata Gap” in OKE - Why the dashboard isn’t the source of truth - How to build intelligent triggers to truly control your infrastructure Don’t trust the green light. Trust your code.
To view or add a comment, sign in
-
BetterDB Monitor v0.4.0 is out 🎉 Multi-database support is added, so you can now monitor multiple Valkey/Redis instances from a single deployment. Switch between databases in the UI, and all dashboards, logs, alerts, and webhooks update to that connection. Most teams don't run just one instance - now you don't need separate deployments to monitor them. Also in this release: → Keyless community tier - no license key needed to get started. Community users automatically get early access to upcoming Pro/Enterprise features as they enter beta. → Version update notifications - know when a new release is available without checking GitHub or Docker Hub. Shown in both the logs and UI for visibility. → Connection-scoped webhooks - scope alerts to specific databases instead of getting noise from all of them. We're still in beta, shipping weekly. If you're running Valkey or Redis, give it a try - single Docker container, takes 2 minutes. Alternative deployment options coming in the next few days. https://lnkd.in/d43JBMGM https://lnkd.in/daCYqxBD
To view or add a comment, sign in