When Jake Paul and Mike Tyson stepped into the ring in November 2024, we delivered the fight to roughly 65 million concurrent streams. On an ordinary day our infrastructure takes in more than 2 billion requests and handles upwards of 38 million real-time logging events every second. At that scale, the whole picture doesn’t fit into a single engineering team’s head or onto a single dashboard. With more Live content, Podcasts and real-time streams, our need to understand and update our systems as quickly as possible becomes imperative to provide a good user experience. Understanding the system at this scale and within the time constraints we have, often becomes a daunting exercise in finding a needle in a haystack, in a dark jungle, blindfolded.
This post is about that search for the needle.
The Four hours before a fix
Consider a regression of the kind we see in a given week. The home screen where you see the list of lists of movies, starts taking about 1.5 seconds longer to render. An alert fires. And then we go looking for what caused this issue and how do we fix it?
As it so happens, a regression on the application could be related to anywhere in the system — a client app regression from a new version deployment or A/B test rollout, a load shedding in the infrastructure, a GraphQL query responding with bad data because of a service rollout 6 hops downstream, are all suspects at this point. So, answering “what caused the issue?” needs insights from across the system, and this involves combing through layers of data in a meticulous manner.
The MELT Layer
“What caused the issue?”
Metrics, events, logs and traces, or MELT, in the industry’s shorthand, are the right raw materials for such an investigation, and each of them answers roughly a quarter of the question. We collect observability data across these four pillars, unified into a single observability data layer. This lets us observe our systems in real time, across what our users are experiencing, how our applications are doing and what our backend infrastructure and services are processing and responding to.
MELT — Metrics, Events, Logs and Traces, and how they are captured across the system.
The investigation that follows may look like this in each layer:
What each layer of MELT tells us and what it leaves out
The timeline of an investigation
So how long does it take to investigate an issue, figure out the root cause and fix it, so our users stop running into this issue again?
A timeline of an investigation: ~4 hours
In our chosen example, four hours. Very little of it went into computing anything difficult. Most of it went into reconstructing relationships that our observability systems already knew but we had never written down anywhere a human or machine could simply read and discover.
The disagreement underneath
The instinct here could have been, around 2023, to reach for more data or better dashboards, and while both help at the margins, neither addresses what makes these investigations slow.
It’s 2026 now, so we might say, “hey, agents”, but without the right context delivered to the agents, AI systems hallucinate, and especially when teams are on-call and need a fix “now”, we cannot let inaccurate agentic analysis lead us down the wrong path. We need the right needle, not any needle.
Each of our systems is internally coherent and externally isolated. Atlas, our telemetry platform, knows applications and their time series. Radar, our alerting system, knows thresholds and firing history. incident.io knows severities and responders. PagerDuty knows rotations. Spinnaker knows what shipped and when. And our Slack history knows, in a form no query engine can reach, which engineer solved something very similar last quarter and what they found.
The difficulty is that they do not agree on what they are talking about. A thing that is an application in one system is a service in a second and a cluster in a third, with names differing by a suffix, a casing convention, or a rename only a couple of people remember (SMEs, ring a bell?). Joining across them requires a human carrying the mapping in their head, and that person becomes the bottleneck in such cross-domain investigations. The hard part of end-to-end observability is probably better understood as a data modelling problem.
What we lacked was an explicit, machine-readable agreement about the entities in our operational world.
The Ontology
This agreement has a name: an ontology. Ontology is a formal specification of types, the properties they carry, and the relationships they may have. Its unit is the triple, one fact expressed as subject, predicate, object. Consider the following example triples.
In the example here, we have already encoded six facts, and can answer in one query, a question that used to span three tools.
“Which incidents affected applications owned by Team Bedrock in us-east-1?” is a single query in place of looking up incidents, Spinnaker and Radar.
We call our ontology layer, The Observability Ontology. It acts as the schema, rule set and shared agreement that standardizes how different systems understand entities like applications, services, incidents, deployments, etc.
We built it on the W3C stack for this: RDF for the data model, OWL for the ontology, SHACL for validation, and SPARQL for querying. These standards are well specified and widely implemented, which keeps the model portable across graph stores.
The data from the ontology itself, is stored as a knowledge graph, persisted to QuipuDB, our homegrown RDF graph database, a distributed quad store based on redb, a Rust-based embedded key value db. Data here is generated by parsing raw telemetry, logs, and incident discussions into triples (subject-predicate-object facts).
Every harvest also produces a full Turtle serialization for archival, making it human-readable and diff-friendly, which has proven more useful for debugging than we expected.
Our ontology is organized into twelve namespaces, each owning one operational domain:
There are two decisions about this ontology that mattered while we built it, so they’re worth sharing.
The first is that we refused a generic Service type. “Service” means four different things across our tooling, so applications resolve to exactly one type, ops:Application, and every one of them is validated against our software catalog of roughly 49,000 entries before the entity is created. Unvalidated strings like usernames, team names, dependency names scraped from network topology, never become applications. We also cap dependency edges in both directions, because one popular service can carry thousands of reverse dependencies and would otherwise dominate the graph.
The second decision concerns what we chose to model. Incident chat carries the reasoning behind an incident. The hypotheses tried and discarded, the mitigation that worked, who knew why, is all context in the incident’s Slack channel and that knowledge evaporates when the channel goes quiet. So a 200-message, 50-person, 15-service incident channel becomes a structure, converting the operational chaos of an incident into ordered triples:
Creating determinism from non-determinism
Building this graph means using tools that are inherently stochastic. LLMs summarize discussions. Fuzzy matching resolves service names. A probabilistic engine proposes causal chains. None of these produce the same output twice with certainty, but what comes out at the end of our system is a deterministic graph that answers a SPARQL query the same way every time.
The ontology is the mechanism that creates this determinism through its pipelined approach. We think of the pipeline as a ladder that drives entropy down, where entropy means the proportion of information still trapped in unstructured form. For example, at the start of this pipeline, an LLM could say anything, but it cannot emit a triple that violates the type system. We constrain the output space tightly enough and a stochastic process yields a deterministic contract:
# The model may produce any prose. Its output MUST fit these predicates: category IN ["incident", "question", "discussion", "decision"] sentiment IN ["positive", "neutral", "negative", "urgent"]
# Result — a deterministic triple: thread:123 slack:category "incident" . thread:123 slack:isIncidentRelated "true"^^xsd:boolean .
There are five layers that get us down this ladder. Any single step of this ladder is non-deterministic. But in aggregate, the chain becomes deterministic. That inversion is the whole trick. We describe them below:
The five layers to create determinism from non-determinism
Enrichers: Agents as entropy reducers
We use what we call, enrichers, to reduce entropy across the knowledge graph. Dozens of enrichers run over the graph. Each one of them takes noisy input and emits typed triples, and each reads what earlier enrichers wrote. The contract for an Enricher is deliberately narrow:
class Enricher: async def _enrich_async(self, ...): raw = await self.fetch(...) # 1. read noisy input result = self.analyze(raw) # 2. process (may be stochastic) graph.add((uri, RDF.type, OPS.X)) # 3. emit TYPED triples graph.add((uri, OPS.pred, Literal(result.value))) return EnrichmentResult(success=True, triples_added=count)
Three invariants that are standard across the enrichers:
every entity carries an rdf:type,
every URI follows a single naming scheme, and
the catalog gates every ops:Application.
Two operational choices are worth naming here, as well. The enrichment pipeline runs sequentially — load the graph, add triples, and save.
This sequencing removes an entire category of race conditions at a cost in wall-clock time we are happy to pay. And calls out to external systems like metrics, logs, events and the rest could be best-effort, so a slow downstream API doesn’t block the graph harvest, albeit some degradation. To that end, we are also working to translate these separate systems into the ontology, such as the Metrics Ontology and the Alerts Ontology, so everything could be queried from the Ontology itself.
The Correlation Engine: finding the correlation chains
The correlation engine assimilates the vast number of signals from diverse input sources and condenses them into causal or correlatory chains of events. Today, the engine reads thirteen signal types, including log errors, metric anomalies, slow and failed traces, alerts, incidents, deployments, config changes, experiments, autoscaling, commits, end-to-end bottlenecks, and error patterns. It then searches for paths among them. Some ordered pairs are known to be causal and carry a high prior weight. For example, deployment before log error, config change before metric anomaly, failed trace before incident, and so on. Others, which show high correlation are considered correlation chains. Such chains are scored on temporal proximity and severity, inside a ten-minute window, with anything under 0.6 confidence getting discarded.
The Correlation Engine
The role of the engine here is to propose with evidence, not to make conclusions. Hence, the engine stores its chains along with the graph records that were used to create them.
A 0.87 chain in the above example, is a strong hypothesis for a human or an agent to check, and the graph records the confidence alongside the claim so that consumers can understand why a chain is what it is and weigh their options to make decisions.
The knowledge flywheel
Each harvest cycle turns the same four steps, and each rotation leaves the graph richer for the next. This is the Knowledge Flywheel, leading to continuous enrichment and improvement of the context about our system, stored in the knowledge graph.
The Knowledge Flywheel
Observe collects the signals. Enrich adds typed triples. Infer finds patterns invisible in the raw data. Adapt feeds knowledge back, so that established patterns become detection rules. The compounding result of the four phases leads to a self-improving system. A causal chain discovered in one incident becomes a known pattern for the next, and the ontology gradually stops being a schema and starts being a memory.
Consider the example above. When we first see an incident in api-gateway caused due to a timeout and resolve it, we convert this knowledge into entries to the ontology. This becomes encoded knowledge. As we go through multiple cycles of this encoding, the graph starts learning patterns. After N cycles, patterns become wisdom — “api-gateway deploys cause 40% of SEV-2s” and “30% of deployment issues in api-gateway come from Connection Timeouts”. Surface this as an insight and we proactively start reducing incidents, not just react quickly to new ones.
Putting the graph to work
On top of the graph sits AutoRCA, our internal agentic AI solution, built with a set of specialist agents coordinated around one question. Walking the home page regression from the top of this post, the agentic system’s reasoning chain might look something like this:
Each specialization to the inputs is built by people who understand their respective domain deeply. That division of labour only works if the specialists can pass findings to one another without having to agree on the terms of communication or even have knowledge of each other. The ontology acts as the interface. Each specialist learns one vocabulary — the graph’s. A team writing specialized use cases for how they use traces record their findings as triples about an ops:Application, the logs specialization reads triples about that same ops:Application, and neither needs to know the other exists. A new specialist learns the same vocabulary once and can immediately read everything the other six have written.
Set against the investigation we opened with, the change is narrower than it might sound. The alert still fires at 3:30pm, an engineer still has to weigh the evidence, and the fix still has to be written by someone who understands the code. The hour or two that used to go into assembling context — finding the owning team, reconstructing what shipped, correlating information from different parts of the system, recalling that something similar happened last quarter — all that work is the part the ontology and the graph absorbs. We are early enough that the ranked hypotheses are sometimes wrong, which is why each one carries the evidence that produced it. But when the engineer now starts the investigation, they are already holding the picture that used to take half an afternoon to assemble.
If you want to build one of these
A fair amount of what we have built, we learned by getting it wrong first. Based on this experience, these are the principles we would carry into a second attempt.
Model identity before anything else. Choose one authoritative catalog per entity kind and validate against it before creating an entity.
Treat the ontology as code. Version it, review it, test it, and let changes arrive as pull requests. Treating the ontology as code lends itself to agentic development and lets teams scale and contribute to it.
Separate concepts from instances. Concepts change on a human timescale and instances change continuously, conflating them makes both harder to evolve.
Build on open standards. RDF, OWL, SHACL and SPARQL are specified, implemented many times over, and portable. A bespoke graph model ties a future consumer to one implementation.
Constrain stochastic tools with a type system. This is the principle we would defend hardest. Models are useful precisely where determinism is impossible, and a sufficiently narrow output contract converts their output into something a query engine can trust.
Make enrichment best-effort and ordered. A slow dependency should utmost degrade the graph, never block it. And ordering enrichers so each builds on the last buys more accuracy and that’s worth the tradeoff with parallelism that buys speed.
Model human reasoning alongside machine telemetry. The most valuable edges in our graph came from discussion. Telemetry records what the system did, discussion records what people understood and interpreted. The reasoning is more valuable to store and serve as wisdom when we need it.
Keep humans as the approvers. Let agents propose fixes, enrichers, and ontology changes, and route every proposal through a review process with humans in the loop.
What comes next
The graph makes three things possible. First, automatic root cause analysis, where a regression arrives with a ranked set of graph-backed hypotheses attached. Next, automated remediation, where a sufficiently confident diagnosis carries a proposed action like a targeted rollback, a traffic shift, a code change. And eventually infrastructure that heals itself, acting on a predicted failure before members feel it.
Alongside this, we plan to integrate the Observability Ontology with Upper, the domain-modeling language at the heart of Netflix’s Unified Data Architecture (UDA). Upper lets teams describe business and system domains as interconnected, machine-readable models and preserve their meaning and constraints as those models are projected into concrete schemas and systems. This integration would connect operational concepts such as applications, alerts, deployments, and incidents to the broader set of domains modeled across Netflix, giving engineers and agents a shared semantic layer for querying, validation, and reasoning. We will begin with a proof of concept that models incidents in Upper and use what we learn to determine where the two systems should converge.
Acknowledgements
None of this would exist without the teams who share their data, expertise, and patience with an early system. We want to specially acknowledge the efforts of our own team members who made this possible from very early on in this lifecycle, Kaela Marsheck and Aditya Gaur. We are also especially grateful to our collaborators in Netflix’s ontology working group, Peter Royal, Alex Hutter, and to Alexandre Bertails and members of the Unified Data Architecture (UDA)/Knowledge Foundations team for sharing Upper and their domain-modeling expertise, and for helping us explore how the Observability Ontology can become part of a broader semantic foundation at Netflix.
The positive tests are coming mainly from Western states, including California, Washington and Arizona. Regardless of where you're located in the U.S., though, most people are recommended to get their annual flu shot in September or October, and the same goes for this flu season.
The annual flu shot is the best way to prevent hospitalization and death from seasonal influenza. Nearly everyone ages 6 months and older is recommended to get a flu vaccine, and older adults have several special options, including a high-dose shot, a vaccine containing an extra immune-stimulating ingredient called an adjuvant, and a brand-new mRNA vaccine.
Given that flu season is off to an earlier start this year, our latest poll asks whether you plan to get vaccinated or have gotten vaccinated already. Tell us your thoughts in the comments below.
With the growing number of SRv6 implementations that netlab supports, it made sense to create a set of topologies to help you get started exploring the SRv6 world. The sample topologies include:
In an earlier post, we introduced the TimeSeries Abstraction, our high-throughput, low-latency platform designed to store and query temporal event data at scale. It has since become the operational backbone for Netflix’s largest datasets, which now span multiple petabytes.
However, managing such large datasets in hot storage, tuned for low-millisecond latency access, is not cheap. At Netflix, cost-efficiency is a continuous discipline rather than a temporary initiative. This post shares two solutions that significantly reduce storage costs by continuously moving older, less frequently accessed data into cheaper object storage:
We’ll start with the Kafka/Flink-based design, then describe how we evolved to a native Cassandra-based cold tier.
Challenges
As Netflix’s TimeSeries datasets have expanded, we’ve encountered several compounding challenges:
Growing retention demands: Users often push for increasing data retention to serve a broader range of use cases, which in turn increases the storage costs.
Escalating costs: Maintaining massive, dense clusters for infrequently accessed historical data is prohibitively expensive.
Sparse historical access: Most queries target recent events, but older data must remain readily available for occasional audits and investigations.
Operational strain: High storage density on hot nodes complicates scaling, deployment, and cluster stability.
Latency requirements: Even historical data sometimes requires sub-second query performance for internal tooling and analysis.
This creates a clear opportunity: keep the most recent data on fast storage and migrate older, rarely read data to cheaper storage while preserving a consistent API. Done well, this can meet user and operational needs while greatly reducing our infrastructure spend.
TimeSeries Abstraction
Before we dive into the cold storage solutions, here is a quick recap of how TimeSeries partitions its dataset into multiple tables, each covering a window of time we call Time Slices.
Time Slices and Immutable Windows
This enables several key optimizations, most notably allowing a table to become immutable once it crosses the write boundary, which lets us safely apply further optimizations. For more details, refer to our previous post.
This slice information is critical and is maintained in a dedicated metadata table:
Time Slice Metadata Storage
Creating new Time Slices and maintaining their metadata is a primary responsibility of the TimeSeries control plane. The TimeSeries data plane uses this information to correctly route the read and write traffic to the provisioned tables.
TimeSeries Data and Control Plane
With that background, let’s look at the different solutions we built for data tiering.
Solution 1: Streaming with Apache Kafka and Flink
Our first solution followed a familiar streaming pattern using Apache Kafka and Flink, where we streamed data to S3 in realtime while simultaneously persisting it to Cassandra.
Streaming with Apache Kafka and Flink
Getting Data Into S3 Efficiently
To optimize data streaming to S3, the system hash-partitions Time Series IDs into buckets. This partitions the stream and lets dedicated Flink tasks handle each bucket, ultimately batching and reducing the write throughput to S3. Flink workers batch events into Parquet files grouped by event time within the hour. These files are great for high-throughput streaming writes, but not for low-latency reads.
Enter the Compaction job.
Compaction Job to Optimize Reads
The compaction job runs daily and merges small Parquet files into a single, optimized daily Parquet file per namespace.
The job further optimizes the Parquet files in the following ways:
Small Row Groups: Creates small (4–8 MB) row groups to minimize lookup cost. We can then use the Parquet Java library to perform range reads of row groups from S3. This is intentionally well below Parquet’s recommended row group floor — smaller groups were better because our reads are dominated by point/range lookups rather than full scans.
Event Sorting: Sorts events within a row group by time, eliminating brute-force scans when querying for a time range.
Bloom Filters: Caches Bloom filters and file metadata in EVCache, allowing the TimeSeries query planner to efficiently skip unnecessary reads from S3.
Compacted and Sorted Parquet File Layout
Once the compaction job completes, all compacted files per partition are recorded in the TimeSeries control plane, and the hot storage slices can now be truncated!
On some of our largest datasets, this job typically takes about 12 hours, prioritizing lower compute costs over execution speed.
Compaction Burndown Per Day
Streaming Cold Tier Read
Once compacted files are optimized in cold storage, they are ready to be read. The TimeSeries data plane fans out a given query across both the hot and cold tiers and merges their result. The users still use the same query interface:
TimeSeries Tiered Storage Read Path
The Cold Tier Data Plane uses compaction metadata for aggressive planning and pruning, which dramatically reduces the S3 query fan-out. As a result, retrieving a full day of data requires at most three sequential S3 reads, even in the uncommon case where an ID spans two row groups. This yields a p50 latency of ~150 ms and a p99 latency of ~500 ms per read.
Cold Tier Read Latency in Seconds
At the time of writing, the streaming cold tier implementation was processing up to 1,250 reads per second from cold storage globally:
Cold Tier Read Requests Per Second
Why not stop here?
The streaming cold tier has delivered real savings, shrinking both Cassandra’s EBS footprint and the Cassandra backup footprint in S3. But the streaming infrastructure itself is expensive and adds moving pieces that widen the team’s on-call scope:
Additional Infrastructure and Operational Cost
We wanted to improve this further and asked ourselves:
Can we avoid streaming data into the object store and the associated data transfer costs?
Can we reduce the compute and operational overhead of new components?
That led to a more radical question: Can we read directly from existing Cassandra backups in S3?
Solution 2: Cassandra Cold Storage for Immutable Data
At Netflix, we back up Cassandra data into S3 at regular intervals for disaster recovery. The idea for tiering is simple:
Read directly from these S3 backups instead of paying a separate data transfer cost to S3.
Once TimeSeries marks a hot Time Slice as ready for cold storage, we can customize Cassandra to read directly from its backups, allowing us to delete the on-disk data copy. This would also incur minimal changes to the TimeSeries read path!
New TimeSeries Read Path
To make this viable in production, we had to answer a series of “How…?” questions.
How Can We Read the Backups Efficiently?
We introduced a new compaction strategy in Cassandra, which we call ReadOnlyCompactionStrategy (ROCS). Here is how it works:
Once a time slice crosses the user-configured cold-tier boundary, TimeSeries stops accepting writes for that time slice making it immutable. This immutability unlocks significant optimization opportunities:
SSTable overlap: Standard strategies (LCS, TWCS) often produce SSTables that overlap in the token space, and so a single partition key may exist across multiple files.
Read fan‑out: Because a read must touch every SSTable covering the key’s token range, this overlap turns a single point-read into multiple lookups, increasing latency across all tiers.
ROCS re-compacts all SSTables once, aligning them precisely with token ranges:
ROCS SSTable Boundaries
ROCS applies more broadly to all TimeSeries use cases, driving tail latency down on all storage tiers by reducing the SSTable fan-out during read operations:
Tail Latency Improvement in Reads after ROCS
Once the immutable Time Slices have been optimized, it is time to trigger backups.
How Would We Leverage Backups?
Cassandra backs up every node in the cluster, organizing each backup on S3 as a declarative manifest, along with the SSTables backing the keyspace’s tables:
Alongside the large Data.db file, backups include a compact set of index, metadata, and integrity files. This structure allows us to quickly locate, validate, and restore specific partitions or data ranges without scanning the entire dataset. Furthermore, since the manifest catalogs every S3 path, we can download most of these components directly to the Cassandra node (excluding the Data.db file) where they occupy negligible space. These local components enable efficient query planning before any data is read from S3.
How Would Cassandra Read From Backups?
Cassandra reads typically pull data from two sources: the Memtable, which holds recent unflushed writes, and flushed SSTables on durable storage. As of Cassandra 4.1, we can leverage pluggable Memtables to redirect reads from “cold” tables directly to the backup storage in S3.
-- create table for a given Time Slice CREATE TABLE data_20260616_00_04 ( ... ..., PRIMARY KEY ((time_series_id, time_bucket, event_bucket), event_time, event_id, key) ) WITH CLUSTERING ORDER BY (event_time DESC, event_id DESC, key DESC) ... AND compaction = ... AND compression = ... AND memtable = 'backupmemtable:timestamp=1781953200000' ← Custom memtable ...
The timestamp marks the point at which the backup is guaranteed to cover the full table, and the table is guaranteed to be immutable.
Opening a backup Memtable kicks off a state-machine initialization that pulls the index data from S3 onto the node once:
State Machine for Cassandra Backup Memtable Initialization
This process ensures all SSTable components are downloaded reliably through several resilience mechanisms:
Retries and backoff: All S3 operations and file-system cache writes use retries with exponential backoff.
Resource isolation: Initialization runs on a dedicated thread pool, isolated from normal Cassandra operations.
Partial download protection: If a downloaded component’s size doesn’t match the manifest, it is deleted and re-downloaded.
DDL change protection: A .timestamp file marks completed initialization and ties it to the backup timestamp. If the table’s backup timestamp changes, this file is removed and initialization restarts for the new timestamp.
Read protection: Memtable initialization is asynchronous; reads to an initializing memtable block for at most half the read RPC timeout.
Once initialization has pulled every SSTable component (except Data.db) from S3 onto the local disk, the Memtable is ready to serve reads. Here is what the read process looks like:
Reading from Cassandra Backup Memtables
To minimize latency, the system parallelizes S3 reads across requests, SSTables, and individual chunks. A shared cache deduplicates fetches and prefetches data, so chunks are ready before they are needed.
We further optimize cluster and table settings to reduce the number of S3 calls:
Larger compression chunks: Reduce S3 calls per partition by reading fewer, larger chunks (accepting some over-fetch on small reads).
The TimeSeries control plane automates the entire transition as slices cross the immutable boundary:
Automating Time Slice Lifecycle Transitions
Each slice can be in any lifecycle stage at any time without impacting system availability, and the system remains resilient to delayed backups or lagging compactions.
How Would Cassandra Handle The Additional Load?
Serving data from S3 is much more CPU-intensive than reading from local storage. A sudden spike in read requests can overwhelm the Cassandra cluster and affect the hot path, turning a cost-saving feature into a reliability risk.
To avoid this, cold reads can optionally run on a separate Cassandra cluster that points its backup Memtables at the hot cluster’s backups:
Isolated Cold Tier Cassandra Topology
The cold Cassandra cluster must match the original cluster in topology but need not match it in hardware! We can run the cold cluster on much smaller, cheaper, storage-light instances.
Real World Performance
Cassandra Cold Tier was rolled out to the most storage‑intensive TimeSeries Abstraction clusters. It currently stores over 15 PB of compressed data in Cassandra cold storage and has allowed us to cut attached storage significantly on all affected clusters:
Storage Reduction During Rollout: from 80% disk usage to 7%
At the same time, it serves thousands of reads per second from cold tier storage in any given region:
Cold Tier Read Requests Per Second
Cassandra Cold Tier improves p90 latency by ~30% over the previous cold storage implementation, thanks to more efficient S3 access patterns and smaller read sizes, while keeping p99 latency under ~500 ms. Cassandra already handles bursts of cold reads gracefully via its chunk cache, so this design preserves that same resilience for the S3-backed cold tier:
Cold Tier Read Latency: blue — p90 latency, brown — p99 latency; latency measured in seconds)
A couple of months ago Jim Highsmith explained how
practitioners writing for each other forms a distinct style of writing. He
now adds a section to the article with more detail on the craft of how we
write in this style. How the writer shows up in the prose, its four
principles, the role of story, how to sensibly use AI, three levels of
editing, and clarity of the audience.
Over its five-year history, P99 CONF has hosted quite a few speakers who’ve offered pointed takedowns of the namesake metric. At last year’s conference, Adrian Cockcroft didn’t explicitly state that P99s are BS…but he did allude to it. If you don’t know Cockcroft, he’s spent decades architecting, scaling, and optimizing resilient, high-performance systems at giants like Sun Microsystems, Netflix…
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