graph-node VS turbo-geth

A subgraph according to The Graph (which is a decentralized protocol for indexing and querying blockchain data) is a custom API built on blockchain data. They are queried using the GraphQL query language and are deployed to a Graph Node using the Graph CLI.

Event is a log entity which EVM smart contracts can emit during transaction execution. Events are very good at signalling that an some action has taken place on-chain. Applications can subscribe and listen to events to trigger some off-chain logic or they can index, transform and store events in some off-chain storage (look at The Graph protocol or Ethereum ETL).

Subgraph cloned from repo :https://github.com/graphprotocol/graph-node/tree/master/docker

https://github.com/scaffold-eth/scaffold-eth-2 this repo comes with wagmi and hard hat preconfigured you can use it as a sample , but if you are large querying data I would advise you the graph https://thegraph.com/

The Graph

There are also decentralized network services like The Graph or Pocket Network that provide decentralized access to Ethereum and other blockchain networks. Note that these services primarily provide APIs for querying blockchain data, and may not provide full functionality for sending transactions or executing smart contracts like a full Ethereum node or services like Infura and Alchemy.

For read-only the way to use The Graph

See it for yourself.

thegraph.com is the only site operated by The Graph Foundation, and is the centrepiece for all activity on the protocol. Can a website/web dApp be hacked? Yes. Has it been hacked in the past two years since it launched? No.

Check out TheGraph, most of DEX has a subgraph indexing data

To be clear, it was a CPU fault that doesn't occur at all when running e.g. stress-ng, but only (as far as I know) when running our particular production workload.

And only after several hours of running our production workload.

But then, once it's known to be provokeable for a given machine, it's extremely reliable to trigger it again — in that it seems to take the same number of executed instructions that utilize the faulty part of the die, since power on. (I.e. if I run a workload that's 50% AES-NI and 50% something else, then it takes exactly twice as long to fault as if the workload was 100% AES-NI.)

And it isn't provoked any more quickly, by having just provoked it with the last hard-fault — i.e. there's no temporal locality to it. Which would make both "environmental conditions" and "CPU is overheating / overvolting" much less likely as contributing factors.

> There have been enough of them in private hands for long enough that if there were widespread issues they would be well-known.

Our setup is likely a bit unusual. These machines that experienced the faults, have every available PCIe lane (other than the few given to the NIC) dedicated to NVMe; where we've got the NVMe sticks stuck together in software RAIDO (meaning that every disk read fans in as many almost-precisely-parallel PCIe packets contending for bus time to DMA their way back into the kernel BIO buffers.) On top of this, we then have every core saturated with parallel CPU-bottlenecked activity, with a heavy focus on these AES-NI instructions; and a high level of rapid allocation/dellocation of multi-GB per-client working arenas, contending against a very large and very hot disk page cache.

I'll put it like this: some of these machines are "real-time OLAP" DB (Postgres) servers. And under load, our PG transactions sit in WAIT_LWLOCK waiting to start up, because they're actually contending over acquiring reader access to the global in-memory pg_locks table in order to write their per-table READ_SHARED locks there (in turn because they're dealing with wide joins across N tables in M schemas where each table has hundreds of partitions and the query is an aggregate so no constraint-exclusion can be used.) Imagine the TLB havoc going on, as those forked-off query workers fight for time.

It's to the point that if we don't either terminate our long-lived client connections (even when not idle), or restart our PG servers at least once a month, we actually see per-backend resource leaks that eventually cause PG to get OOMed!

The machines that aren't DB servers, meanwhile — but are still set up the same on an OS level — are blockchain nodes, running https://github.com/ledgerwatch/erigon, which likes to do its syncing work in big batches: download N blocks, then execute N blocks, then index N blocks. The part that reliably causes the faults is "hashing N blocks", for sufficiently large values of N that you only ever really hit during a backfill sync, not live sync.

In neither case would I expect many others to have hit on just the right combination of load to end up with the same problems.

(Which is why I don't really believe that whatever problem AMD might have seen, is related to this one. This seems more like a single-batch production error than anything, where OVH happened to acquire multiple CPUs from that single batch.)

> It's possible that AMD didn't order enough capacity from TSMC to meet demand, and couldn't get more during the COVID supply chain issues.

Yes, but that doesn't explain why they weren't able to ramp up production at any point in the last four years. Even now, there are still likely some smaller hosts that would like to buy EPYC 7xxxs at more-affordable prices, if AMD would make them.

You need an additional factor to explain this lack of ramp-up post-COVID; and to explain why the cloud providers aren't still buying any 7xxxs (which they would normally do, to satisfy legacy clients who want to replicate their exact setup across more AZs/regions.) Server CPUs don't normally have 2-year purchase commitments. It's normally more like 6.

Sure, maybe Zen4c was super-marketable to the clouds' customers, so they negotiated with AMD to drop all their existing spend commitments on 7xxx parts purchases in favor of committing to 9xxx parts purchases. (But why would AMD agree to that, without anything the clouds could hold over their head? It would mean shutting down many of the 7xxx production lines early, translating to the CapEx for those production lines not getting paid off!)

consensus client/execution client -> ERIGON v2.45.2 and NIMBUS v23.5.1

This file (https://github.com/ledgerwatch/erigon/blob/devel/cmd/prometheus/dashboards/erigon.json) is outdated. Many panels are not working. Would appreciate if someone can share the json with all the useful panels.

48 hours - currently on stage 7 - https://github.com/ledgerwatch/erigon/blob/devel/eth/stagedsync/README.md

https://github.com/ledgerwatch/erigon 696 contributors

Erigon 2.41.0 does come with Shanghai for mainnet though so as long as people are running 2.41.0 or 2.42.0 they will be all set.

— Erigon ***(~10.8% of all clients)***A fork of Geth client (also in the GO programming language) that is focused on maximizing the efficiency of storage for archive nodes.

Github issue tracking it, likely to be fixed on Erigon side