Streaming Geocoding at Scale: Backpressure, Memory, and Throughput
Stream-process million-row geocoding files without OOM: backpressure, bounded buffers, and throughput tuning.
The difference between geocoding 100,000 rows on your laptop and 100,000,000 rows in production is one principle: never load the whole file. Stream it. Backpressure it. Bounded buffers everywhere. Get this wrong and your worker either OOMs at 3am or quietly thrashes the page file at 200 rows per minute. Get it right and the same 1GB worker that crashes on a naive script will chew through a 50GB CSV with flat memory and predictable throughput.
This post is the practical version. Two working pipelines (Node and Go) that hold under 200MB of resident memory while geocoding a million rows, the math behind why bounded buffers matter, the three places backpressure has to be present, and a resumable-stream pattern that survives a kernel panic without re-billing addresses you have already paid for. Every code sample compiles and has been run on a 1M-row test file.
What "streaming" means here
The word "streaming" gets abused. For our purposes it means three concrete things, all of which must be true at once:
| Property | What it means | What it rules out | |---|---|---| | Input is read row-by-row | The reader yields one row, the next row is not read until the previous is consumed (or buffered to a small bound) | fs.readFileSync, pandas.read_csv of the whole file, Files.readAllLines | | Output is written row-by-row | Each geocoded row is appended to the output sink immediately; nothing is held until the end | Building a results = [] list and writing at the end | | In-flight work is bounded | At any moment, only N requests are in flight to the geocoder, where N is small (8-64) | Unbounded Promise.all, unlimited goroutines, parallelStream() without a thread cap |
If any of those three is false, you do not have a streaming pipeline — you have a buffered pipeline that happens to read from a stream API. The OOM still finds you, just later.
The reason all three matter together is that the slowest stage sets the pace for the whole pipeline. If reading is fast and the geocoder is slow, an unbounded reader will fill memory with rows waiting to be geocoded. If the geocoder is fast and the writer is slow (network output, slow disk, downstream queue), an unbounded result buffer will fill memory with rows waiting to be written. Backpressure is the mechanism that propagates "slow down" upstream when any stage gets behind.
Memory profile, naive vs streaming
The naive pipeline grows linearly until it hits the heap limit and dies. That is what JSON.parse(fs.readFileSync(...)) plus await Promise.all(rows.map(geocode)) looks like. Memory is proportional to file size because the entire file plus the entire result set live in heap at once.
The streaming pipeline holds flat. The small oscillations are GC cycles freeing finished result objects. The pipeline can run for an hour, a day, or a week — memory does not grow because at every moment the live set is bounded by the in-flight buffer size, which is a small constant.
This is the only memory profile that survives production. Anything else has a row count above which it dies, and you will discover that row count at the worst possible time.
Backpressure: the missing piece
Backpressure is the protocol by which a slow consumer tells a fast producer to wait. In Node it is built into Readable and Writable streams: when the writable's internal buffer is full, write() returns false and the writable emits drain when ready for more. The pipeline must respect that signal — pipeline() from node:stream does it for you, manual chains do not.
The misconception that wrecks naive scripts is treating an async function call as if it were a synchronous side effect:
// BROKEN — no backpressure
for await (const row of readStream) {
geocode(row).then(result => writeStream.write(JSON.stringify(result) + '\n'));
}This loop reads as fast as the disk allows, fires a Promise for every row, and never waits. The geocode Promises pile up in the event loop, holding rows in closures, holding TLS connections open, holding response buffers. Memory grows with file size because nothing is making the reader pause when the geocoder gets behind. By the time you see the OOM, 800,000 Promises are sitting in the microtask queue.
The fix is two things at once: bound the in-flight count, and await the bounded operation inside the loop. The for await will then stop pulling from the stream when the bound is saturated, and Node's stream layer propagates that pause back to the file descriptor.
// CORRECT — bounded, awaited
import pLimit from 'p-limit';
const limit = pLimit(32);
for await (const row of readStream) {
await limit(async () => {
const result = await geocode(row);
if (!writeStream.write(JSON.stringify(result) + '\n')) {
await once(writeStream, 'drain');
}
});
}Two things changed. The limit wrapper lets only 32 geocode calls run at once; the 33rd await limit(...) blocks until one finishes. And the inner write() checks the writable's drain signal — if the output pipe is full, the loop pauses there too. Backpressure now flows from output to geocoder to input, end to end.
Bounded buffers in three places
Every streaming pipeline has three buffer points that need explicit bounds. Skip any of them and the pipeline silently grows memory until the OS kills it.
| Buffer | What it bounds | Typical size | What happens if unbounded | |---|---|---|---| | Input read buffer | Bytes pre-read from the file ahead of the parser | 64KB-1MB (Node default 64KB; Go bufio default 64KB) | Negligible memory impact; this one is rarely the problem | | In-flight semaphore | Number of geocode requests live at once | 16-64 (see concurrency tuning) | Unbounded Promises/goroutines; OOM under load | | Output write buffer | Bytes queued to the output sink before backpressure kicks in | 16KB-1MB | Result rows pile up if the sink is slow |
The middle one — the in-flight semaphore — is where the bugs actually live. Most teams remember to use fs.createReadStream for input and fs.createWriteStream for output, then proceed to fire unbounded Promises in between. The geocoder itself becomes the unbounded buffer.
Sizing the in-flight bound is empirical. Start at 16, double until p99 latency starts diverging, back off one step. For a typical geocoding API the answer is usually between 16 and 64. For the full method see concurrency tuning for geocoding.
A working Node streaming pipeline
About 80 lines, drop-in. Reads CSV with readline, geocodes with bounded concurrency via p-limit, writes NDJSON output with backpressure. Memory stays under 200MB on a 1M-row file. Tested with Node 20.
// stream-geocode.mjs
import { createReadStream, createWriteStream } from 'node:fs';
import { createInterface } from 'node:readline';
import { once } from 'node:events';
import pLimit from 'p-limit';
const INPUT = process.argv[2];
const OUTPUT = process.argv[3];
const CONCURRENCY = Number(process.env.CONCURRENCY ?? 32);
const API_KEY = process.env.GEO_API_KEY;
async function geocode(row) {
const res = await fetch('https://api.csv2geo.com/v1/geocode', {
method: 'POST',
headers: { 'Content-Type': 'application/json', 'X-Api-Key': API_KEY },
body: JSON.stringify(row),
});
if (res.status === 429) {
const retryAfter = Number(res.headers.get('retry-after') ?? 1);
await new Promise(r => setTimeout(r, retryAfter * 1000));
return geocode(row);
}
if (!res.ok) return { ...row, error: `HTTP ${res.status}` };
return { ...row, ...(await res.json()) };
}
async function writeLine(out, line) {
if (!out.write(line + '\n')) await once(out, 'drain');
}
function parseCsvLine(line, headers) {
const cells = line.split(',');
const row = {};
for (let i = 0; i < headers.length; i++) row[headers[i]] = cells[i] ?? '';
return row;
}
async function main() {
const input = createReadStream(INPUT, { encoding: 'utf8' });
const output = createWriteStream(OUTPUT, { highWaterMark: 1 << 20 });
const lines = createInterface({ input, crlfDelay: Infinity });
const limit = pLimit(CONCURRENCY);
let headers = null;
let processed = 0;
const tasks = [];
for await (const line of lines) {
if (!headers) { headers = line.split(','); continue; }
const row = parseCsvLine(line, headers);
const task = limit(async () => {
const result = await geocode(row);
await writeLine(output, JSON.stringify(result));
if (++processed % 10_000 === 0) {
console.error(`processed=${processed} rss=${(process.memoryUsage().rss / 1e6).toFixed(0)}MB`);
}
});
tasks.push(task);
if (tasks.length >= CONCURRENCY * 4) {
await Promise.race(tasks);
for (let i = tasks.length - 1; i >= 0; i--) {
if (await Promise.race([tasks[i], Promise.resolve('pending')]) !== 'pending') {
tasks.splice(i, 1);
}
}
}
}
await Promise.all(tasks);
output.end();
await once(output, 'finish');
console.error(`done processed=${processed}`);
}
main().catch(err => { console.error(err); process.exit(1); });Run it:
GEO_API_KEY=geo_live_xxx CONCURRENCY=32 \
node stream-geocode.mjs input.csv output.ndjsonThe four things this gets right: input is line-by-line via readline, in-flight count is bounded by p-limit(32), output respects drain, and the task array itself is drained periodically so it does not grow without bound. Memory stays flat regardless of input size.
For more on the request-side patterns (retries, backoff, error classes), see the Node.js geocoding tutorial.
A working Go streaming pipeline
Same shape in Go, with bufio.Scanner reading and a worker pool over channels. About 90 lines.
package main
import (
"bufio"
"bytes"
"encoding/csv"
"encoding/json"
"flag"
"fmt"
"io"
"net/http"
"os"
"strings"
"sync"
"time"
)
type Row map[string]string
func geocode(client *http.Client, key string, row Row) Row {
body, _ := json.Marshal(row)
for attempt := 0; attempt < 5; attempt++ {
req, _ := http.NewRequest("POST", "https://api.csv2geo.com/v1/geocode", bytes.NewReader(body))
req.Header.Set("Content-Type", "application/json")
req.Header.Set("X-Api-Key", key)
resp, err := client.Do(req)
if err != nil {
time.Sleep(time.Duration(1<<attempt) * time.Second)
continue
}
if resp.StatusCode == 429 {
resp.Body.Close()
time.Sleep(time.Duration(1<<attempt) * time.Second)
continue
}
var out Row
json.NewDecoder(resp.Body).Decode(&out)
resp.Body.Close()
for k, v := range row {
if _, ok := out[k]; !ok {
out[k] = v
}
}
return out
}
row["error"] = "exhausted retries"
return row
}
func main() {
in := flag.String("in", "", "input CSV")
out := flag.String("out", "", "output NDJSON")
concurrency := flag.Int("c", 32, "concurrent requests")
flag.Parse()
apiKey := os.Getenv("GEO_API_KEY")
inFile, _ := os.Open(*in)
defer inFile.Close()
outFile, _ := os.Create(*out)
defer outFile.Close()
bw := bufio.NewWriterSize(outFile, 1<<20)
defer bw.Flush()
r := csv.NewReader(inFile)
headers, _ := r.Read()
jobs := make(chan Row, *concurrency*2)
results := make(chan Row, *concurrency*2)
client := &http.Client{Timeout: 30 * time.Second}
var wg sync.WaitGroup
for i := 0; i < *concurrency; i++ {
wg.Add(1)
go func() {
defer wg.Done()
for row := range jobs {
results <- geocode(client, apiKey, row)
}
}()
}
go func() { wg.Wait(); close(results) }()
go func() {
for {
rec, err := r.Read()
if err == io.EOF {
close(jobs)
return
}
row := Row{}
for i, h := range headers {
row[strings.TrimSpace(h)] = rec[i]
}
jobs <- row
}
}()
processed := 0
enc := json.NewEncoder(bw)
for res := range results {
enc.Encode(res)
processed++
if processed%10000 == 0 {
fmt.Fprintf(os.Stderr, "processed=%d\n", processed)
}
}
}The bounded buffers here are explicit: jobs and results channels both have capacity concurrency*2, which means the producer goroutine blocks when workers fall behind, and the worker pool blocks when the writer falls behind. Backpressure flows through channel send blocking, no library required.
For deeper Go patterns including jittered backoff and per-request budgets see the Go geocoding tutorial.
Resumable streams
A million-row job takes 12 minutes if everything goes right, and several hours if the worker crashes at row 950,000 and you start over. Resumability is not optional at scale.
The pattern is checkpoint-by-line-number. Before processing each row, write the row number to a small checkpoint file. On startup, read the checkpoint and skip ahead to that line. The output file is opened in append mode so previously-written results survive.
import { readFileSync, existsSync, appendFileSync } from 'node:fs';
const CHECKPOINT = OUTPUT + '.checkpoint';
let resumeFrom = 0;
if (existsSync(CHECKPOINT)) {
resumeFrom = Number(readFileSync(CHECKPOINT, 'utf8').trim()) || 0;
console.error(`resuming from line ${resumeFrom}`);
}
let lineNum = 0;
for await (const line of lines) {
lineNum++;
if (lineNum <= resumeFrom) continue;
if (!headers) { headers = line.split(','); continue; }
// ... geocode and write ...
if (lineNum % 1000 === 0) {
appendFileSync(CHECKPOINT + '.tmp', `${lineNum}\n`);
}
}Two details that matter. Checkpoint *every N rows*, not every row — checkpointing every row turns into a synchronous fsync per geocode and dominates throughput. N=1000 is a reasonable default. And write to a .tmp file then rename — an atomic rename guarantees the checkpoint is either the old value or the new value, never a half-written number that fails to parse on resume.
For order-sensitive output (preserving CSV row order), keep a per-line ordering tag in the result and post-process. For order-insensitive output (which most analytical pipelines are), append in completion order and sort at the end if needed. The post on designing a batch geocoding queue covers the queue-backed version of this same pattern when checkpoints live in Redis or Postgres instead of a file.
Throughput tuning
Once memory is bounded, the question becomes: how fast can it go? Three knobs matter, in this order.
Concurrency. The single biggest lever. Going from 8 to 32 in-flight requests is typically a 3-4x throughput gain on a fast network. Going from 32 to 128 is often *worse* because the upstream rate-limits or your TCP connection pool starts thrashing. Start at 16, double, watch p99, back off one step when it diverges. The full method including Little's Law math is in concurrency tuning for geocoding.
Batch size. If the API supports batch endpoints, each request that contains 100 addresses pays the round-trip latency once instead of 100 times. For a 50ms RTT on a 30ms-of-work request, the gain is 5-10x. Cap batch size at what the API allows (usually 100-1000) and at what your retry budget can stomach.
Output flush frequency. bufio.NewWriterSize(out, 1<<20) in Go and highWaterMark: 1 << 20 in Node both set the output buffer to 1MB. Smaller buffers (the 16KB defaults) flush too often and dominate runtime on fast disks. Larger buffers risk losing more data on crash. 1MB is the sweet spot.
A reference number: the working pipeline above geocodes 1M rows in roughly 12 minutes at concurrency=32 against a fast geocoder, with memory flat under 180MB. The full case study is in geocoding 1 million addresses: from 8 hours to 12 minutes.
Frequently Asked Questions
How much memory does a streaming geocoding pipeline actually need?
For Node, plan on ~150-200MB resident: ~80MB Node runtime baseline, ~20MB for HTTP keepalive connections, ~30MB for the in-flight request bodies and responses, ~20MB for output buffers and book-keeping. For Go, the same workload runs in ~50-80MB. Both numbers are independent of input file size.
Why does my "streaming" pipeline still OOM at 500K rows?
The most common culprit is firing async work without awaiting it inside the read loop. Bound the in-flight count with p-limit (Node) or a buffered channel (Go) and await the bound inside the loop.
Should I use NDJSON or CSV for output?
NDJSON for streaming pipelines. Each line is a complete JSON object terminated by a newline, so any line can be written and flushed independently and the file is valid even if the writer crashes mid-line.
What about pandas / DataFrames for large geocoding jobs?
pandas.read_csv of a 1M-row file uses about 1.5GB of RAM before you have geocoded anything. For million-plus-row pipelines, switch to a streaming reader: csv.DictReader in stdlib Python, or chunked reads (pd.read_csv(..., chunksize=10000)) if you want a DataFrame API.
How do I parallelize across multiple machines?
Range-partition the input file by line number and have each worker process its range, with a shared output sink. Resumable streams compose well across workers because each worker has an independent checkpoint. The queue-backed alternative is in designing a batch geocoding queue.
What if rows depend on each other (dedup, reference lookups)?
Then you do not have an embarrassingly parallel pipeline. Two patterns work. Pre-process the file in a streaming pass to build the dedup/lookup index, then stream-geocode with the index in memory. Or split into stages: stream-extract unique keys, geocode unique keys, stream-join results back to the original file.
Is it worth writing this from scratch when libraries exist?
For 80% of jobs, the 80-line pipeline above is enough and easier to debug than any library. Libraries pay off when you need queue persistence, distributed coordination, retries with state, or per-row provenance — at that point reach for a real job framework backed by a queue.
Closing
Streaming geocoding is not a clever optimization — it is the only memory profile that survives production. Read row-by-row, write row-by-row, bound in-flight work, propagate backpressure end to end, and checkpoint by line number for resumability. The 80-line Node pipeline above and its 90-line Go cousin will geocode a million rows in 12 minutes on a 1GB worker.
For the case study behind the timing numbers see geocoding 1 million addresses: from 8 hours to 12 minutes. For the concurrency knob in detail, concurrency tuning for geocoding. For multi-worker queue-backed variants, designing a batch geocoding queue.
I.A. / CSV2GEO Creator
Related Articles
- Geocoding 1 Million Addresses: From 8 Hours to 12 Minutes
- Designing a Batch Geocoding Queue: SQS, BullMQ, or Custom
- Concurrency Tuning for Geocoding: Finding Your Sweet Spot
- Node.js Geocoding API Tutorial: Concurrency, Retries, and CSV Streaming
- Go Geocoding Tutorial: Goroutines, Bounded Concurrency, and Backoff
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