for - Youtube - Digital Drip: The Imperceptible Flows of E-Waste - Filip Vedra - WS23 Planet B UMPRUM - 2025, Jan - adjacency - Deep Humanity - sensory bubble - social norm of producer-consumer split and alienation - spread by Industrial Revolution - hyperobjects - source - Youtube - Digital Drip: The Imperceptible Flows of E-Waste - Filip Vedra - WS23 Planet B UMPRUM - 2025, Jan

// - comment - An insightful documentary that examines the social norm amplified by the Industrial Revolution, - the producer-consumer split and resulting alienation - Globalization has further exasperated this as global supply chains are hyperobjects which no individual can truly sense the scale of

1:23:30 How can a neighbourhood become a local food PRODUCER evolving into a PROSUMER

Producer

for - podcast channel, youtube channel - Andrew Huberman - neuroscience - theme - science-based tools for everyday life - interview with - music producer Rick Ruben - podcast title - How to access your creativity

summary - Although Rick Ruben's field is music production, he shares his perspective on creativity

• for: stats - oil and gas - steep drop in revenues of fossil fuel producer economies

• stats: oil and gas - steep drop in revenues of fossil fuel reliant economies

  • per capita net income from oil and natural gas among producer economies will be 60% lower in 2030 in a 1.5 °C scenario.relative to revenues between 2010 and 2022.

• question

  • many producer economies are not diversifying into clean energy fast enough to compensate for these steep revenue drops

• for: climate change - individual change, producer-consumer entanglement, big oil, COP28

producer key properties => linger time -> linger.ms batch size -> batch.size

these determine the throughput and latency of kafkaproducers

Chen, Cathy Xi, Gordon Pennycook, and David Rand. ‘What Makes News Sharable on Social Media?’ PsyArXiv, 9 July 2021. https://doi.org/10.31234/osf.io/gzqcd.

Deutsch, J., & Barigazzi, J. (2021, April 21). EU preparing legal case against AstraZeneca over vaccine shortfalls. POLITICO. https://www.politico.eu/article/eu-preparing-legal-case-against-astrazeneca-over-vaccine-shortfalls/

Balleer, A., Link, S., Menkhoff, M., & Zorn, P. (2020). Demand or Supply? Price Adjustment during the COVID-19 Pandemic. IZA Discussion Paper, 13568.

Richard Curtis is a screenwriter, producer and film director. He produced Love Actually, Bridget Jone's Diary, Notting Hill and many more rom-coms

t = throughput (QPS) p = single partition for production c = consumption

two scenarios to get the leader back: 1.) Wait to bring the master back online. 2.) Or elect the first node that comes back up. But in this scenario if that replica partition was a bit behind the master then the time from when this replica went down to when the master went down. All that data is Lost.

SO there is a trade off between availability and consistency. (Durability)

This is very important a 1-to-1 mapping between writer and reader with partition. If you have more producers per partition or more consumers per partition your consistency is going to go haywire

High Watermark: messages copied over to log replicas

see how it can hook into the current logging mechanism

May not be needed for our use case.

In flume you loose events if the disk is down. This is very bad for our usecase.

irrespective of the fact that the consumer has consumed the message that message is kept in kafka for the entire retention policy duration.

You can have two or more consumer groups: 1 -> real time 2 -> back up consumer group

Could be something we can consider for directing data from a raw log to a tenant based topic.

Replication Factor means number of nodes/brokers which could go down before we start losing data.

So if you have a replication factor of 6 for a 11 node cluster, then you will be fault tolerant till 5 nodes go down. After that point you are going to loose data for a particular partition.

ordering is guaranteed.

Kafka maintains the ordering for you...

Producer can publish to a specific topics

Each Partition is bounded by the server that hosts that partition.

partition offset maintained by kafka. Offset number is maintained so that if the consumer goes down nothing breaks.