Discover and read the best of Twitter Threads about #graph
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Always feels great to close allocation & reward our awesome #delegators🤗
$GRT fam, we are open for delegation. Stake with us today for high performance & ~13% consistent #APR🔥
Delegate: thegraph.com/explorer/profi…
@graphprotocol #Web3 #graph $GRT (1/6)🧵
$GRT fam, we are open for delegation. Stake with us today for high performance & ~13% consistent #APR🔥
Delegate: thegraph.com/explorer/profi…
@graphprotocol #Web3 #graph $GRT (1/6)🧵
We have special channel on discord for graph-delegators where we discuss everything around #subgraph, @graphprotocol and analytics 🤝
Join now for any questions🚀
Discord: dapplooker.com/community (2/6)🧵
Join now for any questions🚀
Discord: dapplooker.com/community (2/6)🧵
Our effective cut is under 10% so delegators gets the maximum benefit.
Learn about our indexer infrastructure and operations here 👇
@graphopsxyz #Decentralization #indexer (3/6)🧵
Learn about our indexer infrastructure and operations here 👇
@graphopsxyz #Decentralization #indexer (3/6)🧵
#SQL vs. #Cypher for hierarchies
Recursive common-table expressions (SQL)
vs.
built-in path variables in #graph
Recursive common-table expressions (SQL)
vs.
built-in path variables in #graph
This only shows part of the picture; with recursive CTEs in #SQL I believe it is the case that:
- You can constrain max recursion, but not minimum without writing extra code
Contrast to cypher:
MATCH (e:Employee)-[:REPORTS_TO*3..5]->(:Employee)
- You can constrain max recursion, but not minimum without writing extra code
Contrast to cypher:
MATCH (e:Employee)-[:REPORTS_TO*3..5]->(:Employee)
recursive CTEs are a good example of "implementing a graph abstraction on top of something else". Namely the employee table represents a bunch of node instances; the managerID is a relationship, and the CTE implements a graph traversal.
so it's common to have data elements like a "food" with some related information (here, it's "nutrition information"). When using a document database you basically have two choices:
- Store the two documents separately and lookup the other when needed
- Store them together
- Store the two documents separately and lookup the other when needed
- Store them together
Storing them together is great for efficient read operations. But it's bad for updates. If many foods share the same nutrition facts, and you need to update the facts, you have many updates to do to change one simple thing
because you duplicated data in sub-documents
because you duplicated data in sub-documents
So the main typical tradeoff is latency. Batch when you need fresh data in larger volumes, say once per hour/day/week/month
Stream when time value of data is high/immediate and you can't afford to be more than minutes behind
Stream when time value of data is high/immediate and you can't afford to be more than minutes behind
The overall event queue (so to speak) that's being ingested has a total velocity. Let's say it's
- 1M events/day
- ~42k events/hour
- ~694 events/min
- ~69 events/sec
Let's say 2kb per event, or roughly 2gb/day, 138kb/sec.
- 1M events/day
- ~42k events/hour
- ~694 events/min
- ~69 events/sec
Let's say 2kb per event, or roughly 2gb/day, 138kb/sec.
Me hace muchísima ilusión presentaros el curso de verano que organizamos @andortizg y un servidor para @UNIAuniversidad:
🤖💻🧠
Introducción práctica a la Inteligencia Artificial y el Deep Learning.
🤖💻🧠
unia.es/inteligencia.a…
#IA #DL #ST
Procedo a vender la moto :)
🤖💻🧠
Introducción práctica a la Inteligencia Artificial y el Deep Learning.
🤖💻🧠
unia.es/inteligencia.a…
#IA #DL #ST
Procedo a vender la moto :)
Inteligencia Artificial… Redes Neuronales…
¡¡LAS MÁQUINAS NOS QUITAN EL TRABAJO!!
Dos campos permean las ciencias en el siglo XXI: la #estadística y la #computación. Combinados, dan lugar al Aprendizaje Automático, el Machine Learning. O como hacer que las máquinas "aprendan"
¡¡LAS MÁQUINAS NOS QUITAN EL TRABAJO!!
Dos campos permean las ciencias en el siglo XXI: la #estadística y la #computación. Combinados, dan lugar al Aprendizaje Automático, el Machine Learning. O como hacer que las máquinas "aprendan"
Como cuento en los monólogos, las máquinas más bien nos quitan "trabajo".
El Machine Learning es herramienta fundamental para automatizar procesos, y hoy en día no se puede entender sin las redes neuronales (la base del Deep Learning #DL).
El Machine Learning es herramienta fundamental para automatizar procesos, y hoy en día no se puede entender sin las redes neuronales (la base del Deep Learning #DL).
موضوع: تحلیل سبد سرمایه گذاری 15 شرکت معتبر سرمایه گذاری #کریپتوکارنسی
نتایج:
1-اعلام #رمزارز های مشترک در #پرتفوی شرکتها
2-معرفی پروژه ها و توکن های فوق ، پتانسیل رشد و ارائه توضیحات شخصی
در صورتی که محتوا مناسب و دارای جنبه های آموزشی برای سایرین است لطفا #ریتویت کنید.
نتایج:
1-اعلام #رمزارز های مشترک در #پرتفوی شرکتها
2-معرفی پروژه ها و توکن های فوق ، پتانسیل رشد و ارائه توضیحات شخصی
در صورتی که محتوا مناسب و دارای جنبه های آموزشی برای سایرین است لطفا #ریتویت کنید.
لیست شرکت های سرمایه گذاری بررسی شده:
#a16z #polychain #boostvc #dcg #hashkey #pantera#Placeholder_Ventures #cms_holding #blockchain #Coinfund#fabric_ventures#CMS_Holdings #binance_labs#Multicoin#DragonFly
#a16z #polychain #boostvc #dcg #hashkey #pantera#Placeholder_Ventures #cms_holding #blockchain #Coinfund#fabric_ventures#CMS_Holdings #binance_labs#Multicoin#DragonFly
خیلی مهم- رمزارزهای ذیل صرفا انتخاب کارشناسان شرکت های فوق است و زمان خرید آنها توسط شرکت های فوق فاکتور خیلی مهمی است، به نفع شماست که بدون تحلیل، تحقیق و بررسی، آنها را معیار سرمایه گذاری خود قرار ندهید.
توکن های مشترک در پرتفوی سرمایه گذاری شرکتهای بررسی شده:
توکن های مشترک در پرتفوی سرمایه گذاری شرکتهای بررسی شده:
Seems like a lot of #graph visualization stuff cues off of humans' tendency to want to reason about things in terms of either time, or space.
In a force-directed layout, effectively you have an x/y axis and you're reasoning about the graph in space, where "distance" is used as a proxy for path length.
there are also a lot of Google Earth representations, that try to render the spatial view as more tangible
"Crap on a map" has worked really well for a long time because brains are good at reasoning about known physical spaces
"Crap on a map" has worked really well for a long time because brains are good at reasoning about known physical spaces
Halin v0.11 was just released, with significant new stuff! Also a new UI design. Let's jump in (thread)
Cluster members exist in their own slide-out menu. The "tab per member" approach wasn't working with bigger clusters. Now you have room to grow.
It's now possible (with most recent #APOC) to get storage capacity metrics, so you can see how close you are to filling your disk which tends to make @neo4j very unhappy. Thanks to @mesirii and @santand84 for several things that helped make this possible.
