InfluxData Internals by Ryan Betts
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InfluxData builds a time series platform primarily deployed for DevOps and IoT monitoring. This talk presents several lessons learned while scaling the platform across a large number of deployments—from single server open source instances to highly available high-throughput clusters. This talk presents a number of failure conditions that informed subsequent design choices. Ryan Betts (Director of Engineering at InfluxData) will discuss designing backpressure in an AP system with tens of thousands of resource-limited writers; trade-offs between monolithic and service-oriented database implementations; and lessons learned implementing multiple query processing systems.
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InfluxData Internals by Ryan Betts
- 1. Ryan Betts / Dir. Eng Influxdata InfluxDB Internals
- 2. Agenda • 2.0 key differences • Path to 2.0 OSS beta • 2019 internals focus • 1.x release and sustaining model
- 3. TICK Stack Doh • UI • Scrapers • Tasks • Database
- 4. Discontinuous Query • Flux + Cron • Multi-query Flux scripts
- 6. User model kicks the bucket • Org + Bucket + User Replaces: • DB + RP • Fine Grained Auth • Enterprise Authz • OSS Authz
- 7. Underscore Infernal • Prometheus style metrics
- 8. Out of time partitioning • Retention Policy • Database • ShardGroup • Shard
- 9. Only in our memory index • TSI only in 2.0
- 10. Transpiling On • Flux • InfluxQL • PromQL • Apache Arrow
- 11. CLI • ./influx for API access • ./influxd for local tools
- 12. Nits picked • UTF-8 tags • Float -Inf/+Inf/Nan
- 13. Agenda • 2.0 key differences • Path to 2.0 OSS beta • 2019 internals focus • 1.x release and sustaining model
- 14. Path to 2.0 OSS Beta • Weekly Alpha releases adding new functionality + testing • Features • InfluxQL Transpiler • DELETE with predicate • Bulk Import (1.x, 2.x) • Bulk Export • Community process (issue templates, GitHub labels, milestone communication…)
- 15. Flux Release Train • Weekly releases • Deployed to Cloud2 • Weekly with 2.0 OSS Alpha • Monthly with 1.7.x InfluxDB • https://github.com/influxdata/flux/releases
- 16. Agenda • 2.0 key differences • Path to 2.0 OSS beta • 2019 internals focus • 1.x release and sustaining model
- 17. 2019 Internals Points of Emphasis • Community responsiveness • DELETE correctness • Load-shedding and back-pressure • Query resource limits
- 18. TSM Observations • Write amplification rarely a concern • Compaction memory & cpu utilization often a concern • Backfilling is common - as a special case of bulk load • Range deletes with a predicate are common • Offline tooling is surprisingly popular • TSM space efficiency can be very variable
- 20. “Hinted handoff is sadness” - Me for the last 20 months
- 21. Agenda • 2.0 key differences • Path to 2.0 OSS beta • 2019 internals focus • 1.x release and sustaining model
- 22. 2019 Release Train for 1.x • Monthly InfluxDB releases for 1.7, 1.6, 1.5 (on demand) • Chronograf releases paired with InfluxDB 1.7 (for Flux) • Kapacitor released as necessary • 1.8 InfluxDB release as vehicle for Flux GA
Hinweis der Redaktion
- Want to highlight not just the product and the time to awesome aspects of this - but also the operational and community and impacts. UI / DB in the same process space. If you monitor influxdb internals, you’ll see UX, Tasks, Scrapers, outbound-io, etc. Consolidates not just the products into a single binary but also consolidates our Chronograf, Kapacitor, InfluxDB GitHub activity into the `influxdb` repo. We have some new practices to learn / develop in support now that these previously separate components run in a single process space.
- Few people like the continuous query experience: * too hard to write / try / observe * too hard to debug * too hard to re-run on failure * too hard to report run results
- OSS, Enterprise storage nodes, Enterprise meta nodes, … all different interfaces. Now consolidated.
- OSS, Enterprise 1.x have different permission systems. Some changes require the ./influx CLI; some are available via REST. None of those REST endpoints are well documented (a criticism of development - not our docs team!) Shipped FGA to “fake” some separation of tenants — but it adds complexity that’s not worth its weight.
- Retention v. Series creation. Database physical arrangement vs. bucket logical arrangement.
- - designed to support powerful language services to drive graphical user experiences that can't be created at a reasonable engineering expense otherwise. We want to remove the programming from time series and present a better UX - designed to integrate across disparate datasources (a key argument here is that SQL systems are compatible and integration code is necessary -- so why not make integration a first class language design feature) - ability to cross-compile; time-series is all about eco-system and we want to embrace prom, sql, flux, arrow, ... all on a single optimizer and access path engine. So we wrote the underlying language tools to do that. - in addition to integrating other syntaxes, we want to integrate with other programming and analytics environments -- like Jupyter notebooks. We want to integrate with those using arrow - for efficiency, scale, and interoperability - and using the query syntax the user wants. - we need a 4GL data scripting language for alerting and other time series use cases. We don't PLSQL. Barf. Other SQL systems also reach for non-SQL languages when they needs this in stored procedure engines (javascript, Lua, java..)
- Personal Background: 20 years building infrastructure software as an engineer, technical lead, CTO, and director of engineering. Have worked with at two DB companies maturing 1.0 products Talk is about a few things: scaling some of the softer side and also some specific InfluxDB internals
- Community: ACES, DevRel + client code, Community Manager, InfluxDB triage with PM Delete: DML not Query code path
- TLA Model
- Always the victim Complicates load shedding — more critical ADDS I/O in a reduced functional state Exacerbates “thundering herd” on recovery Is only a partial solution anyway In our case, is much less dense than compacted blocks; not efficient Example of doing more work with telegraf replaying timed-out data leading to more merging Also a design flaw in current influx that deletes are processed by the query engine, not the write path and don’t flow through HH - so HH can be cause of inconsistency.
- Personal Background: 20 years building infrastructure software as an engineer, technical lead, CTO, and director of engineering. Have worked with at two DB companies maturing 1.0 products Talk is about a few things: scaling some of the softer side and also some specific InfluxDB internals