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Browse Geocoding Tutorials, Guides & Updates
Batch vs Realtime Geocoding: Picking the Right Default
The two patterns for getting addresses geocoded look completely different and someone always asks "which one do we need?" The honest answer is "you probably need both, but only one matters for the immediate decision." This post is the practical version: when batch makes sense, when realtime makes sense, the cost math, and the architecture that lets a single pipeline serve both modes without a rewr...
| May 17, 2026
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Exponential Backoff for Geocoding: When to Retry, When to Stop
A geocoding pipeline that retries everything aggressively is the same pipeline that quadruples its API bill during a brief upstream outage and then hits the provider's circuit breaker, making the outage look longer than it actually was. A pipeline that doesn't retry at all is the pipeline that loses 5% of its rows on a 30-second blip and you find out two days later from a customer. The right shape...
| May 16, 2026
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Rate Limiting a Geocoding Pipeline: Token Bucket vs Leaky Bucket vs Sliding Window
Your geocoding pipeline starts hitting 429 Too Many Requests. The provider's documented limit is "1,000 requests/minute." You're sending in bursts of 20 across 30 workers, but you're also re-trying on errors, and the math doesn't quite add up to "1,000/min." Welcome to client-side rate limiting — the part of the pipeline you only think about after a production incident. This post is the practical...
| May 15, 2026
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Caching Strategies for Geocoding: Redis, SQLite, or In-Process
You've decided to cache your geocoding calls. The question is where. Three answers cover ~99% of real pipelines: an in-process LRU map, a SQLite file on disk, or Redis. Each has a clear sweet spot. Pick wrong and you'll either burn money on Redis Cloud you didn't need or hit a wall when the in-process cache evaporates on every deploy. This post is the practical comparison: scale ranges, latency, p...
| May 14, 2026
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Idempotent Geocoding: Why and How to Make Calls Safe to Retry
A geocoding pipeline that isn't idempotent is a pipeline that double-charges you, double-writes results to your warehouse, and silently corrupts data the day a worker crashes between "API call succeeded" and "result written to DB." Idempotency isn't optional — it's the property that lets you retry on every failure without consequences. Done right, it's invisible. Done wrong, you find out about it...
| May 13, 2026
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Deduplicating Geocoded Addresses: Stable Keys and Fuzzy Matching
You buy a marketing list of 50,000 addresses. You geocode it. The bill is $25. A week later you buy a "fresh" list from the same vendor. You geocode it. Bill is $25 again. You compare the lists: 18,000 of those 50,000 are the same addresses written differently. You just paid $9 to geocode the same buildings twice. This post is the deduplication playbook. Stable hash keys for the easy 80%, fuzzy ma...
| May 12, 2026
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Address Parsing Before Geocoding: Cleaning Inputs for Better Matches
The single biggest match-rate improvement most geocoding pipelines never make is parsing the address before sending it. The team running the pipeline assumes the geocoder will figure out "123 Main St Apt 4B Brooklyn NY 11201"; it usually does. They also assume it will figure out " Apt 4B / 123 main street Brooklyn, NEW YORK 11201-1234, USA "; this is where match rates start to slip. Every quirk...
| May 11, 2026
Read Address Parsing Before Geocoding: Cleaning In...
Designing a Batch Geocoding Queue: SQS, BullMQ, or Custom
A geocoding pipeline that handles a million addresses on a Tuesday morning is not a script with a for loop. It is a queue, a pool of workers, a retry policy, a dead-letter sink, and a status table. Get the shape right and you handle ten million the same way you handle ten thousand. Get it wrong and you spend the next six months patching edge cases that all share the same root cause: the queue wa...
| Updated May 10, 2026
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p99 Latency in Geocoding: Why Your Average Lies
A geocoding pipeline serving 95% of requests from cache in 5ms and the remaining 5% from upstream in 4 seconds has a mean latency of about 205ms. Put that on a dashboard and it sounds healthy. It is also nearly useless. The number tells you nothing about what 1 in 20 of your users actually experiences — which, in this example, is two orders of magnitude worse than the average suggests. The number...
| May 09, 2026
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Concurrency Tuning for Geocoding: Finding Your Sweet Spot
A geocoding pipeline running at concurrency 1 wastes throughput. The same pipeline running at concurrency 512 wastes money — it eats 429s, amplifies tail latency, and starves its own connection pool. The sweet spot sits between those two failure modes, and most teams either guess at it (usually too low) or ramp it until something breaks (usually too high). Neither approach is necessary. Little's L...
| May 08, 2026
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