v0.6.0 Copyright 2021 CommodityStream, LLC ALll Rights Reserved
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Overview
Maidenlane is divided into 3 segments
-
[1] Network and Transactional Scheduling
-
[2] Lambda/Data Analysis
-
[3] Tracing, Logging and State Management
-
Concerns itself with routing, provisioning, consumer facing aspects of offering a permissioned 'clearing/settlement' gateway (whicih is how we describe our relationship with mining pools and end users, we do not use the term 'MEV' or 'BEV'). An example of a solution that would belong to this category is utilizing mainland China DNS Servers to ensure reduced latency when mining nodes joing the permissioned network and are querying the DNS Service to find the network topology, as we do not utilize the DHT table of the public network, nor would we want to utilize it in a private fashion as it would divulgue network mapping and reduce overall security.
-
Deals with the Matching engine proper, 'lambda' or 'delta' calculations, and transaction arrangement. The heart of this system utilizes an in-emory database called KDB+, with its programming language, Q. This is not open source. KDB+ requires a commerical license for running their 64bit database, which starts at 25,000 a year.
-
Our current logging and tracing utilities are being redesigned with a new framework and approach that is being used by IOHK (Cardano Foundation), called
Convariant Functor Tracing
. Our existing stack however is being re-implemented to use NixOS as a Package and build service, along with utilizing Dhall for configuration. We do not use Kubernetes for [2] or [3].
-
Additional points of specilization
-
[Geth]
-
modified for enhanced capture at both the filter and data type level
-
embedded changes to offer reduced latency *
-
-
[Parity]
-
Services RLP decoding for incoming messages. A Seperate service runs for this
-
-
[Kafka]
-
Kafka directly interfaces over RPC, there is no need to place a Geth/Parity node infront of Kafka in order to process or transform incoming transactions, etc.
-
Everything utilizes Avro
-
While we have currently implemented a Kafka logger, we have defined an abstract interface that could theoretically support a wide variety of messaging systems.
-
Capturing Write Operations
In the Go Ethereum codebase, there is a
Database
interface which must support the
following operations:
-
Put
-
Get
-
NewBatch
-
Has
-
Delete
-
Close
and a Batch interface which must support the following operations:
-
Put
-
Write
-
Delete
-
Reset
-
ValueSize
We have created a simple CDC wrapper, which proxies operations
to the standard databases supported by Go Ethereum, and
records Put
, Delete
, and
Batch.Write
operations through a
LogProducer
interface. At present, we have
implemented a KafkaLogProducer
to record write
operations to a Kafka topic.
The performance impact to the Go Ethereum server is minimal. The CDC wrapper is light weight, proxying requests to the underlying database with minimal overhead. Writing to the Kafka topic is handled asynchronously, so write operations are unlikely to be delayed substantially due to logging. Read operations will be virtually unaffected by the wrapper.
Replaying Write Operations
We also have a modified Go Ethereum service which uses a
LogConsumer
interface to pull logs from Kafka and
replay them into KDB+
The index of the last written record is also recorded in the database, allowing the service to resume in the event that it is restarted.
Preliminary Implementation
In the current implementation we simply disable peer-to-peer connections on the node and populate the database via Kafka logs.
The RPC service in its current state is semi-functional. Many RPC functions default to querying the state trie at the "latest" block. However, which block is deemed to be the "latest" is normally determined by the peer-to-peer service. When a new block comes in it is written to the database, but the hash of the latest block is kept in memory. Without the peer-to-peer service running the service believes that the "latest" block has not updated since the process initialized and read the block out of the database. If RPC functions are called specifying the target block, instead of implicitly asking for the latest block, it will look for that information in the database and serve it correctly.
Despite preliminary successes, there are several potential problems with the current approach. A normal Go Ethereum node, even one lacking peers, assumes that it is responsible for maintaining its database. Occasionally this will lead to replicas attempting to upgrade indexes or prune the state trie. This is problematic because the same operations can be expected to come from the write log of the source node. Thus we need an approach where we can ensure that the read replicas will make no effort to write to their own database.
Proposed Implementation
Other Models Considered
This section documents several other approaches we considered to
achieving our :ref:design-goals
. This is not
required reading for understanding subsequent sections, but may
help offer some context for the current design.
Higher Level Change Data Capture
Rather than capturing data as it is written to the database, one option we considered was capturing data as it was written to the State Trie, Blockchain, and Transaction Pool. The advantage of this approach is that the change data capture stream would be reflective of high level operations, and not dependent on low level implementation details regarding how the data gets written to a database. One disadvantage is that it would require more invasive changes to consensus-critical parts of the codebase, creating more room for errors that could effect the network as a whole. Additionally, because those changes would have been made throughout the Go Ethereum codebase it would be harder to maintain if Go Ethereum does not incorporate our changes. The proposed implementation requires very few changes to core Go Ethereum codebase, and primarily leverages APIs that should be relatively easy to maintain compatibility with.
Shared Key Value Store
Before deciding on a change-data-capture replication system, one option we considered was to use a scalable key value store, which could be written to by one Ethereum node and read by many. Some early prototypes were developed under this model, but they all had significant performance limitations when it came to validating blocks. The Ethereum State Trie requires several read operations to retrieve a single piece of information. These read operations are practical when made against a local disk, but latencies become prohibitively large when the state trie is stored on a networked key value store on a remote system. This made it infeasible for an Ethereum node to process transactions at the speeds necessary to keep up with the network.
Extended Peer-To-Peer Model
One option we explored was to add an extended protocol on top of the standard Ethereum peer-to-peer protocol, which would sync the blockchain and state trie from a trusted list of peers without following the rigorous validation procedures. This would have been a substantially more complex protocol than the one we are proposing, and would have put additional strain on the other nodes in the system.
Replica Codebase from Scratch
One option we considered was to use Change Data Capture to record change logs, but write a new system from the ground-up to consume the captured information. Part of the appeal of this approach was that we have developers interested in contributing to the project who don’t have a solid grasp of Go, and the replica could have been developed in a language more accessible to our contributors. The biggest problem with this approach, particularly with the low level CDC, is that we would be tightly coupled to implementation details of how Go Ethereum writes to LevelDB, without having a shared codebase for interpreting that data. A minor change to how Go Ethereum stores data could break our replicas in subtle ways that might not be caught until bad data was served in production.
In the proposed implementation we will depend not only on the underlying data storage schema, but also the code Go Ethereum uses to interpret that data. If Go Ethereum changes their schema and changes their code to match while maintaining API compatibility, it should be transparent to the replicas. It is also possible that Go Ethereum changes their APIs in a way that breaks compatibility, but in that case we should find ourselves unable to compile the replica without fixing the dependency, and shouldn’t see surprises on a running system.
Finally, by building the replica service in Go as an extension to the existing Go Ethereum codebase, there is a reasonable chance that we could get the upstream Go Ethereum project to integrate our extensions. It is very unlikely that they would integrate our read replica extensions if the read replica is a separate project written in another language.
Design Goals
Health Checks
A major challenge with existing Ethereum nodes is evaluating the health of an individual node. Generally nodes should be considered healthy if they have the blockchain and state trie at the highest block, and are able to serve RPC requests relating to that state. If a node is more than a couple of blocks behind the network, it should be considered unhealthy.
Service Initialization
One of the major challenges with treating Ethereum nodes as disposable is the initialization time. Conventionally a new instance must find peers, download the latest blocks from those peers, and validate each transaction in those blocks. Even if the instance is built from a relatively recent snapshot, this can be a bandwidth intensive, computationally intensive, disk intensive, and time consuming process.
In a trustless peer-to-peer system, these steps are unavoidable. Malicious peers could provide incorrect information, so it is necessary to validate all of the information received from untrusted peers. But given several nodes managed by the same operator, it is generally safe for those nodes to trust eachother, allowing individual nodes to avoid some of the computationally intensive and disk intensive steps that make the initialization process time consuming.
Ideally node snapshots will be taken periodically, new instances will launch based on the most recent available snapshot, and then sync the blockchain and state trie from trusted peers without having to validate every successive transaction. Assuming relatively recent snapshots are available, this should allow new instances to start up in a matter of minutes rather than hours.
Additionally, during the initialization process services should be identifiable as still initializing and excluded from the load balancer pool. This will avoid nodes serving outdated information during initialization.
Load Balancing
Given reliable healthchecks and a quick initialization process, one challenge remains on loadbalancing. The Ethereum RPC protocol supports a concept of "filter subscriptions" where a filter is installed on an Ethereum node and subsequent requests about the subscription are served updates about changes matching the filter since the previous request. This requires a stateful session, which depends on having a single Ethereum node serve each successive request relating to a specific subscription.
For now this can be addressed on the client application using
Provider Engine’s Filter Subprovider <https://github.com/MetaMask/provider-engine/blob/master/subproviders/filters.js>+
The Filter Subprovider mimics the functionality of installing a filter on a node and requesting updates about the subscription by making a series of stateless calls against the RPC server. Over the long term it might be beneficial to add a shared database that would allow the load balanced RPC nodes to manage filters on the server side instead of the client side, but due to the existence of the Filter Subprovider that is not necessary in the short term.
Reduced Computational Requirements
As discussed in :ref:initialization
, a collection
of nodes managed by a single operator do not have the same trust
model amongst themselves as nodes in a fully peer-to-peer
system. RPC Nodes can potentially decrease their computational
overhead by relying on a subset of the nodes within a group to
validate transactions. This would mean that a small portion of
nodes would need the computational capacity to validate every
transaction, while the remaining nodes would have lower resource
requirements to serve RPC requests, allowing flexible scaling
and redundancy.
Implementation
In go-ethereum/internal/ethapi/backend.go
, a
Backend interface is specified. Objects filling this interface
can be passed to ethapi.GetAPIs()
to return
[]rpc.API
, which can be used to serve the Ethereum
RPC APIs. Presently there are two implementations of the Backend
interface, one for full Ethereum nodes and one for Light
Ethereum nodes that depend on the LES protocol.
This project will implement a third backend implementation, which will provide the necessary information to ethapi.GetAPIs() to in turn provide the RPC APIs.
Backend Functions To Implement
This section explores each of the 26 methods required by the Backend interface. This is an initial pass, and attempts to implement these methods may prove more difficult than described below.
Downloader()
Downloader must return a
*go-ethereum/eth/downloader.Downloader
object.
Normally the Downloader
object is responsible for
managing relationships with remote peers, and synchronizing the
block from remote peers. As our replicas will receive data
directly via Kafka, the Downloader object won’t see much
use. Even so, the PublicEthereumAPI
struct expects to
be able to retrieve a Downloader
object so that it
can provide the eth_syncing
API call.
If the Backend interface required an interface for a downloader
rather than a specific Downloader object, we could stub out at
Downloader that provided the eth_syncing
data based
on the current Kafka sync state. Unfortunately the Downloader
requires a specific object constructed with the following
properties:
-
{mode SyncMode}
- An integer indicating whether the SyncMode is Fast, Full, or Light. We can probably specify "light" for our purposes. -
{stateDb ethdb.Database}
- An interface to LevelDB. Our backend will neeed a Database instance, so this should be easy. -
{mux *event.TypeMux}
- Used only for syncing with peers. If we avoid calling Downloader.Synchronize(), it appears this can safely be nil. -
{
chain BlockChain}
- An object providing the downloader.BlockChain interface. If we only need to support Downloader.Progress(), and we set SyncMode to LightSync, this can be nil. -
{lightchain LightChain}
- An object providing the downloader.LightChain interface. If we only need to support Downloader.Progress(), and we set SyncMode to LightSync, we will need to stub this out and provide CurrentHeader() with the correct blocknumber. -
{dropPeer peerDropFn}
- Only used when syncing with peers. If we avoid calling Downloader.Synchronize(), this can befunc(string) {}
Constructing a Downloader
with the preceding
arguments should provide the capabilities we need to offer the
eth_progress
RPC call.
ProtocolVersion() .
This just needs to return an integer indicating the protocol
version. This tells us what version of the peer-to-peer protocol
the Ethereum client is using. As replicas will not use a
peer-to-peer protocol, it might make sense for this to be a value
like -1
.
SuggestPrice()
Should return a {big.Int}
gas price for a
transaction. This can use
{go-ethereum/eth/gasprice.Oracle}
to provide the same
values a stanard Ethereum node would provide. Note, however, that
gasprice.Oracle requires a Backend object of its own, so
implementing SuggestPrice() will need to wait until the following
backend methods have been implemented:
-
HeaderByNumber()
-
BlockByNumber()
-
ChainConfig()
ChainDb()
Our backend will need to be constructed with an {ethdb.Database}
object, which will be it’s primary source for much of the
information about the blockchain and state. This method will
return that object.
For replicas, it might be prudent to have a wrapper that provides
the {ethdb.Database}
interface, but errors on any
write operations, as we want to ensure that all write operations
to the primary database come from the replication process.
*EventMux() *
This seem to be used by peer-to-peer systems. I can’t find
anything in the RPC system that depends on
EventMux()
, so I think we can return
nil
for the Replica backend.
*AccountManager() *
This returns an *accounts.Manager
object, which
manages access to Ethereum wallets and other secret data. This
would be used by the Private Ethereum APIs, which our Replicas
will not implement. Services that need to manage accounts in
conjunction with replica RPC nodes should utilize client side
account managers such as
+Web3 Provider Engine <https://www.npmjs.com/package/web3-provider-engine>+
_.
In a future phase we may decide to implement an AccountManager service for replica nodes, but this would require serious consideration for how to securely store credentials and share them across the replicas in a cluster.
SetHead()
This is used by the private debug APIs, allowing developers to set the blockchain back to an earlier state in private environments. Replicas should not be able to roll back the blockchain to an earlier state, so this method should be a no-op.
*HeaderByNumber() *
HeaderByNumber needs to return a
*core/types.Header
object corresponding to the
specified block number. This will need to get information from the
database. It might be possible to leverage in-memory caches to
speed up these data lookups, but it must not rely on information
normally provided by the peer-to-peer protocol manager.
This should be able to use core.GetCanonicalHash()
to
get the blockhash, then core.GetHeader()
to get the
Block Number.
BlockByNumber()
BlockByNumber needs to return a
*core/types.Block
object corresponding to the
specified block number. This will need to get information from the
database. It might be possible to leverage in-memory caches to
speed up these data lookups, but it must not rely on information
normally provided by the peer-to-peer protocol manager.
This should be able to use core.GetCanonicalHash()
to
get the blockhash, then core.GetBlock()
to get the
Block Number.
StateAndHeaderByNumber()
Needs to return a *core/state.StateDB
object and a
*core/types.Header
object corresponding to the
specified block number.
The header can be retrieved with
backend.HeaderByNumber()
. Then the stateDB object can
be created with core/state.New()
given the hash from
the retrieved header and the ethdb.Database.
*GetBlock() *
Needs to return a *core/types.Block
given a
common.Hash
. This should be able to use
core.GetBlockNumber()
to get the block number for the
hash, and core.GetBlock()
to retrieve the
*core/types.Block
.
GetReceipts()
Needs to return a core/types.Receipts
given a
common.Hash
. This should be able to use
core.GetBlockNumber()
to get the block number for the
hash, and core.GetBlockReceipts()
to retrieve the
core/types.Receipts
.
GetTd()
Needs to return a *big.Int
given a
common.Hash
. This should be able to use
core.GetBlockNumber()
to get the block number for the
hash, and core.GetTd()
to retrieve the total
difficulty.
*GetEVM() *
Needs to return a *core/vm.EVM
.
This requires a core.ChainContext
object, which in
turn needs to implement:
-
Engine()
- A conensus engine instance. This should reflect the conensus engine of the server the replica is replicating. This would be Ethash for Mainnet, but may be Clique or eventually Casper for other networks. -
GetHeader()
- Can proxybackend.GetHeader()
Beyond the construction of a new ChainContext
, this
should be comparable to the implementation of
eth/api_backend.go’s GetEVM()
Subscribe Event APIs
The following methods exist as part of the Event Filtering system.
-
SubscribeChainEvent()
-
SubscribeChainHeadEvent()
-
SubscribeChainSideEvent()
-
SubscribeTxPreEvent()
As discussed in load-balancing
, the initial
implementation of the replica service will not support the
filtering APIs.
As such, these methods can be no-ops that simply return
nil
. In the future we may implement these methods,
but it will need to be a completely new implementation to
support filtering on the cluster instead of individual replicas.
*SendTx() *
As replica nodes will not have peer-to-peer connections, they will not be able to send transactions to the network via conventional methods.
Instead, we propose that the replica will simply queue transactions onto a Kafka topic. Independent from the replica service we can have consumers of the transaction topic emit the transactions to the network using different methods.
The scope of implementing SendTx()
is limited to
placing the transaction onto a Kafka topic. Processing those
events and emitting them to the network will be discused in
:ref:tx-emitters
.
Transaction Pool Methods
The transaction pool in Go Ethereum is kept in memory, rather
than in the LevelDB database. This means that the primary log
stream will not include information about information about
unconfirmed transactions. Additionally, the primary APIs that
would make use of the transaction pool are the filtering
transactions, which we established in :ref:event-apis
will not be supported in the initial implementation.
For the first phase, this project will not implement the transaction pool. In a future phase, depending on demand, we may create a separate log stream for transaction pool data. For the first phase, these methods will return as follows:
-
GetPoolTransactions() - Return an empty
types.Transactions
slice. -
GetPoolTransaction() - Return nil
-
GetPoolNonce() - Use
statedb.GetNonce
to return the most recent confirmed nonce. -
Stats() - Return 0 transactions pending, 0 transactions queued
-
TxPoolContent() - Return empty
map[common.Address]types.Transactions
maps for both pending and queued transactions.
ChainConfig() .
The ChainConfig property will likely be provided to the Replica Backend as the backend is contructed, so this will return that value.
CurrentBlock()
This will need to look up the block hash of the latest block
from LevelDB, then use that to invoke
backend.GetBlock()
to retrieve the current block.
In the future we may be able to optimize this method by keeping
the current block in memory. If we track when the
LatestBlock
key in LevelDB gets updated, we can
clear the in-memory cache as updates come in.
_tx-emitters:
Transaction Emitters
Emitting transactions to the network is a different challenge than
replicating the chain for reading, and has different security
concerns. As discussed in :ref:send-tx
, replica nodes
will not have peer-to-peer connections for the purpose of
broadcasting transactions. Instead, when the
SendTx()
method is called on our backend, it will log
the transaction to a Kafka topic for a downstream Transaction
Emitter to handle.
Different use cases may have different needs from transaction emitters. On one end of the spectrum, Maidenlane needs replicas strictly for watching for order fills and checking token balances, so no transaction emitters are necessary in the current workflow. Other applications may have high volumes of transactions that need to be emitted.
The basic transaction emitter will watch the Kafka topic for transactions, and make RPC calls to transmit those messages. This leaves organizations with several options for how to transmit those messages to the network. Organizations may choose to:
-
Not to run a transaction emitter at all, if their workflows do not generate transactions.
-
Run a transaction emitter pointed to the source server that is feeding their replica nodes.
-
Run a transaction emitter pointed to a public RPC server such as Infura.
-
Run a separate cluster of light nodes for transmitting transactions to the network
Security Considerations
The security concerns relating to emitting transactions are different than the concerns for read operations. One reason for running a private cluster of RPC nodes is that the RPC protocol doesn’t enable publicly hosted nodes to prove the authenticity of the data they are serving. To have a trusted source of state data an organization must have trusted Ethereum nodes. When it comes to emitting transactions, the peer-to-peer protocol offers roughly the same assurances that transactions will be emitted to the network as RPC nodes. Thus, some organizations may decide to transmit transactions through APIs like Infura and Etherscan even though they choose not to trust those services for state data. = Introduction
For a service to be treated as a commodity, it typically has the following properties:
-
It can be load-balanced, and any instance can serve any request as well as any other instance.
-
It has simple health checks that can indicate when an instance should be removed from the load balancer pool.
-
When a new instance is started it does not start serving requests until it is healthy.
-
When a new instance is started it reaches a healthy state quickly.
Eisting Ethereum nodes don’t fit well into this model:
-
Certain API calls are stateful, meaning the same instance must serve multiple successive requests and cannot be transparently replaced.
-
There are numerous ways in which an Ethereum node can be unhealthy, some of which are difficult to determine.
-
A node might be unhealthy because it does not have any peers
-
A node might have peers, but still not receive new blocks
-
A node might be starting up, and have yet to reach a healthy state
-
-
When a new instance is started it generally starts serving on RPC immediately, even though it has yet to sync the blockchain. If the load balancer serves request to this instance it will serve outdated information.
-
When new instances are started, they must discover peers, download and validate blocks, and update the state trie. This takes hours under the best circumstances, and days under extenuating circumstances.
As a result it is often easier to spend time troubleshooting the problems on a particular instance and get that instance healthy again, rather than replace it with a fresh instance.
Publicly Hosted Ethereum RPC Nodes
Many organizations are currently using publicly hosted Ethereum RPC nodes such as Infura. While these services are very helpful, there are several reasons organizations may not wish to depend on third party Ethereum RPC nodes.
First, the Ethereum RPC protocol does not provide enough information to authenticate state data provided by the RPC node. This means that publicly hosted nodes could serve inaccurate information with no way for the client to know. This puts public RPC providers in a position where they could potentially abuse their clients' trust for profit. It also makes them a target for hackers who might wish to serve inaccurate state informatino.
Second, it means that a fundamental part of an organization’s system depends on a third party that offers no SLA. RPC hosts like Infura are generally available on a best effort basis, but have been known to have significant outages. And should Infura ever cease operations, consumers of their service would need to rapidly find an alternative provider.
Hosting their own Ethereum nodes is the surest way for an organization to address both of these concerns, but currently has significant operational challenges. We intend to help address the operational challenges so that more organizations can run their own Ethereum nodes. = Operational Requirements
The implementation discussed in previous sections relates directly to the software changes required to help operationalize Ethereum clients. There are also ongoing operational processes that will be required to maintain a cluster of master / replica nodes.
{cluster-initialization}
Cluster Initialization
Initializing a cluster comprised of a master and one or more replicas requires a few steps.
 In most circumstances, this should be a typical Geth fast sync with standard garbage collection arguments.
{_leveldb-snapshots}
LevelDB Snapshotting
Once the master is synced, the LevelDB directory needs to be snapshotted. This will become the basis of both the subsequent master and the replica servers.
 Without the "archive" garbage collection mode, the state trie is kept in memory, and not written to either LevelDB or Kafka immediately. If state data is not written to Kafka immediately, the replicas have only the chain data and cannot do state lookups. The master should also be configured with a Kafka broker and topic for logging write operations.
Replica Configuration
Replicas should be created with a copy of the LevelDB database
snapshotted in :ref:leveldb-snapshots
. When executed,
the replica service should be pointed to the same Kafka broker and
topic as the master. Any changes written by the master since the
LevelDB snapshot will be pulled from Kafka before the Replica
starts serving HTTP requests.
Periodic Replica Snapshots
When new replicas are scaled up, they will connect to Kafka to pull any changes not currently reflected in their local database. The software manages this by storing the Kafka offset of each write operation as it persists to LevelDB, and when a new replica starts up it will replay any write operations more recent than the offset of the last saved operation. However this assumes that Kafka will have the data to resume from that offset, and in practice Kafka periodically discards old data. Without intervention, a new replica will eventually spin up to find that Kafka no longer has the data required for it to resume.
The solution for this is fairly simple. We need to snapshot the replicas more frequently than Kafka fully cycles out data. Each snapshot should reflect the latest data in Kafka at the time the snapshot was taken, and any new replicas created from that snapshot will be able to resume so long as Kafka still has the offset from the time the snapshot was taken.
The mechanisms for taking snapshots will depend on operational infrastructure. The implementation will vary between cloud providers or on-premises infrastructure management tools, and will be up to each team to implement (though we may provide additional documentation and tooling for specific providers).
Administrators should be aware of Kafka’s retention period, and be sure that snapshots are taken more frequently than the retention period, leaving enough time to troubleshoot failed snapshots before Kafka runs out
Periodic Cluster Refreshes
Because replication requires the master to write to LevelDB with
a garbage collection mode of "archive", the disk usage for each
node of a cluster can grow fairly significantly after the
initial sync. When disk usage begins to become a problem, the
entire cluster can be refreshed following the :ref:cluster-initialization
process.
Both clusters can run concurrently while the second cluster is brought up, but it is important that the two clusters use separate LevelDB snapshots and separate Kafka partitions to stay in sync (they can use the same Kafka broker, if it is capable of handling the traffic).
As replicas for the new cluster are spun up, they will only start serving HTTP requests once they are synced with their respective Kafka partition. Assuming your load balancer only attempts to route requests to a service once it has passed health checks, both clusters can co-exist behind the load balancer concurrently.
Multiple Clusters
Just as multiple clusters can co-exist during a refresh, multiple clusters can co-exist for stability purposes. Within a single cluster, the master server is a single point of failure. If the master gets disconnected from its peers or fails for other reasons, its peers will not get updates and become stale. A new master can be created from the last LevelDB snapshot, but that will take time during which the replicas will be stale.
With multiple clusters, when a master is determined to be unhealthy its replicas could be removed from the load balancer to avoid stale data, and additional clusters could continue to serve current data.
High Availability
A single cluster provides several operational benefits over running conventional Ethereum nodes, but the master server is still a single point of failure. Using data stored in Kafka, the master can recover much more quickly than a node that needed to sync from peers, but that can still lead to a period of time where the replicas are serving stale data.
To achieve high availability requires multiple clusters with independent masters and their own replicas. Multiple replica clusters can share a high-availability Kafka cluster. The following formula can be used to determine the statistical availability of a cluster:
- math
-
a = 1 - (1 - \frac{mtbf}{mttr + mtbf})^N
Where:
-
mtbf
- Mean Time Between Failures - The average amount of time between failures of a master server -
mttr
- Mean Time To Recovery - The average amount of time it takes to replace a master server after a failure -
N
- The number of independently operating clusters
The values of mtbf
and mttr
will
depend on your operational environment. With our AWS
CloudFormation templates, we have established an
mttr
of 45 minutes when snapshotting daily. We have
not gathered enough data to establish a mtbf, but with two
independent clusters and a 45 minute mttr
,
EC2’s regional SLA becomes the bounding factor of
availability if the mtbf
is greater than two weeks.
This formula focuses only on the availability of masters - it
assumes that each master has multiple independent replicas. If a
master only has a single replica, that will hurt the
mtbf
of the cluster as a whole.
GreyPool
Stratum WebSocket with TLS (uri) `stratumss:// `
Transaction Pool Feeds
This fork of Geth includes two new types of subscriptions, available through the eth_subscribe method on Websockets.
Rejected Transactions
Using Websockets, you can subscribe to a feed of rejected transactions with:
{"id": 0, "method": "eth_subscribe", "params":["rejectedTransactions"]}
This will immediately return a payload of the form:
{"jsonrpc":"2.0","id":0,"result":"$SUBSCRIPTION_ID"}
And as messages are rejected by the transaction pool, it will send additional messages of the form:
{
"jsonrpc": "2.0",
"method": "eth_subscription",
"params": {
"subscription": "$SUBSCRIPTION_ID",
"result": {
"tx": "$ETHEREUM_TRANSACTION",
"reason": "$REJECT_REASON"
}
}
}
One message will be emitted on this feed for every transaction rejected by the transaction pool, excluding those rejected because they were already known by the transaction pool.
It is important that consuming applications process messages quickly enough to keep up with the process. Geth will buffer up to 20,000 messages, but if that threshold is reached the subscription will be discarded by the server.
The reject reason corresponds to the error messages returned by Geth within the txpool. At the time of this writing, these include:
-
invalid sender
-
nonce too low
-
transaction underpriced
-
replacement transaction underpriced
-
insufficient funds for gas * price + value
-
intrinsic gas too low
-
exceeds block gas limit
-
negative value
-
oversized data
However it is possible that in the future Geth may add new error types that could be included by this response without modification to the rejection feed itself.
Dropped Transactions
Using Websockets, you can subscribe to a feed of dropped transaction hashes with:
{"id": 0, "method": "eth_subscribe", "params":["droppedTransactions"]}
This will immediately return a payload of the form:
{"jsonrpc":"2.0","id":0,"result":"$SUBSCRIPTION_ID"}
And as messages are dropped from the transaction pool, it will send additional messages of the form:
{
"jsonrpc": "2.0",
"method": "eth_subscription",
"params": {
"subscription": "0xe5fa5d3c8ec05953bd746a784cfeade6",
"result": {
"txhash": "$TRANSACTION_HASH",
"reason": "$REASON"
}
}
}
One message will be emitted on this feed for every transaction dropped from the transaction pool.
It is important that consuming applications process messages quickly enough to keep up with the process. Geth will buffer up to 20,000 messages, but if that threshold is reached the subscription will be discarded by the server.
The following reasons may be included as reasons transactions were rejected:
-
underpriced-txs: Indicates the transaction’s gas price is below the node’s threshold.
-
low-nonce-txs: Indicates that the account nonce for the sender of this transaction has exceeded the nonce on this transction. That may happen when this transaction is included in a block, or when a replacement transaction is included in a block.
-
unpayable-txs: Indicates that the sender lacks sufficient funds to pay the intrinsic gas for this transaction
-
account-cap-txs: Indicates that this account has sent enough transactions to exceed the per-account limit on the node.
-
replaced-txs: Indicates that the transaction was dropped because a replacement transaction with the same nonce and higher gas has replaced it.
-
unexecutable-txs: Indicates that a transaction is no longer considered executable. This typically applies to queued transaction, when a dependent pending transaction was removed for a reason such as unpayable-txs.
-
truncating-txs: The transaction was dropped because the number of transactions in the mempool exceeds the allowable limit.
-
old-txs: The transaction was dropped because it has been in the mempool longer than the allowable period of time without inclusion in a block.
-
updated-gas-price: The node’s minimum gas price was updated, and transactions below that price were dropped.
MEV-Geth
/**
* @AddMevBundle
* @summary pool mevBundles
* @param {AddMevBundle} <uint64> - ddMevBundle adds a mev bundle to the pool
* @return {mevBundles} blockNumber, minTimestamp, maxTimestamp
*/
func (pool *TxPool) AddMevBundle(txs types.Transactions, blockNumber *big.Int,
minTimestamp, maxTimestamp uint64) error {
pool.mu.Lock()
defer pool.mu.Unlock()
pool.mevBundles = append(pool.mevBundles, mevBundle{
txs: txs,
blockNumber: blockNumber,
minTimestamp: minTimestamp,
maxTimestamp: maxTimestamp,
})
/**
* MevBundles
* @readonly list of bundles valid for the given blockNumber/blockTimestamp
* {uint64} ([]types.Transactions)
*/
// MevBundles returns a list of bundles valid for the given
blockNumber/blockTimestamp
// also prunes bundles that are outdated
func (pool *TxPool) MevBundles(blockNumber *big.Int, blockTimestamp uint64)
([]types.Transactions, error) {
return nil, nil
}
Operational Topics (concerning strategies, etc)
This section describes (without much context), some of the mathmatical principles used to find opportunites in the market.
A good analogy would be comparing this to the well known `A * ` Algorithim for path finding. These equations are utilized, amongst others, as a hearuisistc. We do not make any claims on their formal soundness, only on their current and projected rate of returns.
Super Liquidity Manifolds and Abstract Liquid Tranches
*Paramaratized Constant Function Markets
Super-liquidity manifold (SLM) system is a mathematical construct, defined below to describe an efficient digital market model. Assets that are traded on such market \(^{1}\) may benefit from the trade option against at least one super-liquid exchange medium.
Consider an abstract liquid tranche (ALT) system as a weighted directed graph \(G:=(V, E, w),\) where set of vertices \(V,|V| \leq| N |\) contains digital representation of all tradeable assets in \(G,\) set of edges \(E=\{e \in V \times V:\) \(w(e)>0\}\) represents all possible atomic \(^{2}\) asymmetric \(^{3}\) trades, which are weighted by the function \(w: E \rightarrow R ^{+}\) corresponding to the price of some trade \(e \in E\)
Definition 1
Vertex \(v \in V\) represents half-liquid asset \(^{4}\) iff either \(\operatorname°^{-}(v)=0\) (source) or \(\operatorname°^{}(v)=0(\operatorname{sink}),\) where \(\operatorname{deg}^{(-1)}: V \rightarrow N\) is respectively a number of tail ends (indegree) and a number of head ends (outdegree) from vertices adjacent to \(v\).
Corollary 1.1 - liquid vertex.
Any liquid vertex \(v \in V\) has both \(\operatorname°^{-}(v) \geq 1\) and \(\operatorname°^{+}(v) \geq 1\)
Corollary 1.2 - liquid graph. If there exists a strongly connected subgraph \(G^{\prime} \subseteq G\) s.t. all of its vertices are liquid, then \(G^{\prime}\) is called liquid graph.
Corollary 1.3 - k-liquid graph.
If \(G^{\prime} \subseteq G\) is a k-connected liquid graph, then \(G^{\prime}\) is called \(k\) -liquid. Trade paths can have different liquidity preferences. For example, if a path \((s, v): s, v \in V\) on graph \(G\) has preferable liquidity when compared to any other path \(\left(s^{\prime}, v\right): s^{\prime}, v \in V,\) then \((s, v)\) is a shorter or equally weighted path than \(\left(s^{\prime}, v\right)\) iff \(\sum_{e \in(s, v)} w(e) \leq \sum_{e \in\left(s^{\prim
Definition 2 - preferable liquidity path.
Let \(S \subset V \times V\) contain all shortest paths from vertex \(s\) to vertex \(t: \forall s, t \in V\). Also let vertex \(v \in V\) have the maximal \(^{3}\) betweenness centrality measure \(C_{B}(v):=\sum_{s \neq t \neq v \in V} \frac{\sigma_{s t}(v)}{\sigma_{s t}}: \forall(s, t) \in S,\) where \(\sigma_{s t}:=\sum_{(s, t) \in S} \sum_{e \in(s, t)} w(e)\) and \(\sigma_{s t}(v)\) is a sum of only those shortest paths in \(S\) which contain \(v\). We say that \((s, t) \in S\) is a path with preferable liquidity if it ends with \(v,\) i.e. \(t=v\) In order to capture a desired super-liquidity property of an always preferable asset in an ALT-system \(G,\) we need to identify such asset not only as a preferable "exit" (sink) vertex, but also as the one that can be consequently traded for any other liquid asset in \(G\) at the most attractive price.
Definition 3 - super-liquidity
A liquid vertex \(v \in V\left(G^{\prime}\right)\) of a complete liquid subgraph \(G^{\prime} \subseteq G\) is called a super-liquid vertex iff any preferable liquidity path \(p=(s, v)\) can be almost surely continued with an efficient trade for any other liquid \(u \in V\left(G^{\prime}\right), u \neq v\) in such a way that \(\sum_{e \in(s, u)} w(e) \leq \sum_{e \in(s, v)} w(e)+\sum_{e \in(v, u)} w(e)\) and \((s, u)\) is a shortest path.
Corollary 3.1 - super-liquid graph.

Last definition of a super-liquid graph provides us with a starting point for the future framework of the super-liquidity manifold (SLTM system that can in theory allow efficient price trading. However there is no practical duality between super-liquid and illiquid assets. Instead, we can choose to link super-liquid vertex with a controlled liquidity asset, that has a programmable dynamic pricing model. We can assert that fully illiquid assets are disconnected from G, since they are not digitally traded and unpractical to consider. We assume that no such asset will exists in the future. Such subgraph is called a super slow and super fast (S3F) liquidity system with at least two liquid tokens (vertices).
[1] almost surely in efficient way [2] no double-spending [3] costs for buying and selling operations are not necessarily equal


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Ethereum (ETH) Medium Term Trend Turns Negative, Bears Eye $160
- Ethereum is currently consolidating losses above the $172 support against the US Dollar.
- Bitcoin price is also consolidating above the key $8,000 support area.
- There is a new connecting bearish trend line forming with resistance near $176 on the hourly chart of ETH/USD (data feed via Kraken).
- The price is likely to continue lower as long as it is below the $180 resistance.
Ethereum price is showing signs of bearish continuation versus the US Dollar, similar to bitcoin. ETH bears seem to be eyeing a test of $164 or even $160.
Ethereum Price Analysis
Recently, there was a downside break in Ethereum below the $180 support against the US Dollar. Moreover, ETH price settled below the $178 support and the 100 hourly simple moving average.
Later, it found support near the $172 level and started an upside correction. There was a break above the $175 resistance area. However, the upward move was capped by the $177-$178 resistance area.
Additionally, the price failed to stay above the 23.6% Fib retracement level of the last key decline from the $186 high to $172 low. At the outset, the price is consolidating above the $172 support and facing many hurdles near $176.
More importantly, there is a new connecting bearish trend line forming with resistance near $176 on the hourly chart of ETH/USD. Above the trend line resistance, the 100 hourly simple moving average is near the $178 level to act as a resistance.
Besides, the main resistance is near the $180 level. It coincides with the 50% Fib retracement level of the last key decline from the $186 high to $172 low. Therefore, Ethereum price must break the $178 and $180 resistance levels to start a decent recovery.
Conversely, the price could struggle to recover and continue lower below $172. If there is a downside break below the $172 support, the price is likely to accelerate lower towards the $166 and $164 support levels.
The main support is near the $160 area, where the bulls are likely to take a stand. Any further losses may perhaps push the price towards the $154 level.
Looking at the chart, Ethereum price is showing a lot of bearish signs below the $178 and $180 resistance levels. Therefore, there remains a risk of more downsides below the $172 support area. Only a close above $180 might negate the bearish view and start a decent recovery in the near term.
ETH Technical Indicators
Hourly MACD – The MACD for ETH/USD is currently flat in the bearish zone.
Hourly RSI – The RSI for ETH/USD is currently struggling to settle above the 50 level.
Major Support Level – $172
Major Resistance Level – $180


Monologue on hadoop file system
Cluster of computers
{:class=“img-responsive”}
Before We start
It’s totally theoritical I have no practical experience in this topic. I want that(practical experience) that’s why I am writing this blog 😉 . And I am totally copying the content of this blog from Hadoop Documentation.
I just googled this " HDFS relaxes a few POSIX requirements to enable streaming access to file system data "
You will find tons of link, You can accuse them of plagiarism, Mine as well.
Let’s start
What is HDFS ?
The Hadoop Distributed File System (HDFS) is a distributed file system. So what is a distributed file system. Your file data will be present on multiple system.
HDFS is built,ok, But what is the use case ?
- Hardware Failure -> HDFS is designed to built on cheap commodity hardware. So this means you cannot run it on Good Hardware. Nope, you will just incur more cost.
- Streaming Data Access -> High data throughput rates. What is throughput. Maximum rate at which data can be processed.
- Large Data Sets -> Tuned to support large files.
- Simple Coherencey model -> Write once read many . But what is coherency.All copies of on data set include always the same data values.
- Moving Computation is Cheaper than Moving Data -> Do calcultion on machine near where is data is stored.
- Portability Across Heterogeneous Hardware and Software Platforms -> Self descriptive.
NameNode and Datanodes.
{:class=“img-responsive”}
What is a Namenode ?
NameNode -> NameNode is a master server that manages the file system namespace and regulates access to files by clients. It feels similar to loadbalancer. HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is split into one or more blocks and these blocks are stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes. Data never flows through NameNode.
Data replication
{:class=“img-responsive”} HDFS stores each file as a sequence of block. There are replicated as well, All blocks except the last one are of same size. An application can support number of number of replicas of a file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once (except for appends and truncates) and have strictly one writer at any time.
Replica Placement.
Rack-aware replica placement policy is to improve data reliability, availability, and network bandwidth utilization. The NameNode determines the rack id each DataNode belongs to via the process outlined in Hadoop Rack Awareness. A simple but non-optimal policy is to place replicas on unique racks. This prevents losing data when an entire rack fails and allows use of bandwidth from multiple racks when reading data. This policy evenly distributes replicas in the cluster which makes it easy to balance load on component failure. However, this policy increases the cost of writes because a write needs to transfer blocks to multiple racks.If the replication factor is greater than 3, the placement of the 4th and following replicas are determined randomly while keeping the number of replicas per rack below the upper limit (which is basically (replicas - 1) / racks + 2).
Replica Selection
Datanodes can be distributed across multiple regions. So replica selection for user is done which datanode is nearest to him.
Communication protocol
Just like HTTP all the communication b/w nodes is based on TCP/IP. A client connects with NameNode machine. It talks the ClientProtocol with the NameNode. The DataNodes talk to the NameNode using the DataNode Protocol. The DataNodes talk to the NameNode using the DataNode Protocol. A Remote Procedure Call RPC abstraction wraps both the Client Protocol and the DataNode Protocol. By design, the NameNode never initiates any RPCs. It only responds to RPC requests by DataNodes or clients.
Data Disk Failure, Heartbeats and Re-Replication
- DataNode sends a Heartbeat message to the NameNode
- NameNode detects this condition by the absence of a Heartbeat message
- NameNode marks DataNodes without recent Heartbeats as dead
- DataNode death may cause the replication factor of some blocks to fall, The NameNode constantly tracks which blocks need to be replicated and initiates replication whenever necessary.
- The time-out to mark DataNodes dead is conservatively long (over 10 minutes by default) in order to avoid replication storm caused by state flapping of DataNodes
Cluster Rebalancing
- There is a data rebalancing scheme
- It can move data from one DataNode to another if the free space on a DataNode falls below a certain threshold
- In the event of a sudden high demand for a particular file, a scheme might dynamically create additional replicas and rebalance other data in the cluster Not yet implemented(Seriously). Might be good intern work. LOL
Data integrity
- HDFS client software implements checksum checking on the contents of HDFS files
- A client creates an HDFS file, it computes a checksum of each block of the file and stores these checksums in a separate hidden file in the same HDFS namespace.
Metadata Disk Failure
- FsImage and the EditLog are central data structures of HDFS
- So NameNode can be configured to support maintaining multiple copies of the FsImage and EditLog
- Synchronous updating of multiple copies of the FsImage and EditLog may degrade the rate of namespace transactions per second that a NameNode can support.
- When a NameNode restarts, it selects the latest consistent FsImage and EditLog to use
- Another option to increase resilience against failures is to enable High Availability using multiple NameNodes either with a shared storage on NFS or using a distributed edit log
Snapshots
- Similar to git commit
What happens when you decrease replication factor ?
When the replication factor of a file is reduced, the NameNode selects excess replicas that can be deleted. The next Heartbeat transfers this information to the DataNode.
Features in HDFS
- File permissions and authentication.
- Rack awareness: to take a node’s physical location into account while scheduling tasks and allocating storage.
- Safemode: an administrative mode for maintenance.
- fsck: a utility to diagnose health of the file system, to find missing files or blocks.
- fetchdt: a utility to fetch DelegationToken and store it in a file on the local system.
- Balancer: tool to balance the cluster when the data is unevenly distributed among DataNodes.
- Upgrade and rollback: after a software upgrade, it is possible to rollback to HDFS’ state before the upgrade in case of unexpected problems.
- Secondary NameNode: performs periodic checkpoints of the namespace and helps keep the size of file containing log of HDFS modifications within certain limits at the NameNode.
- Checkpoint node: performs periodic checkpoints of the namespace and helps minimize the size of the log stored at the NameNode containing changes to the HDFS. Replaces the role previously filled by the Secondary NameNode, though is not yet battle hardened. The NameNode allows multiple Checkpoint nodes simultaneously, as long as there are no Backup nodes registered with the system.
- Backup node: An extension to the Checkpoint node. In addition to checkpointing it also receives a stream of edits from the NameNode and maintains its own in-memory copy of the namespace, which is always in sync with the active NameNode namespace state. Only one Backup node may be registered with the NameNode at once.
- Checkpoint and Backup Node in detail
Commands in HDFS
All commands start with hdfs:
| Commands | Description |
|---|---|
| fs –help ls | lists the usage information along with the options to use the command |
| fs –ls –R /user/username | Returns all the available files and recursively lists all the subdirectories under /user/username |
| fs –mkdir /user/username/directory-name | create a new directory |
| fs –copyFromLocal Sample1.txt /user/username/destination-address | copy from local filesystem to hdfs |
| fs –put Sample2.txt /user/username/destination-address | uploads a single file or multiple source files from local file system to hadoop distributed file system |
| fs –moveFromLocal Sample1.txt /user/username/destination-address | deletes original and copy |
| fs –du /user/username/destination-address | HDFS disk usage command |
| fs –df | Display disk usage of current hadoop distributed file system |
| fs –expunge | empties the trash by deleting all the files and directories |
There a lot of other command similar to unix file system with hadoop fs prefixed.
Command prefix “hdfs “:
| Commands | Description |
|---|---|
| classpath –glob | expands wildcard I don’t know what that is |
| classpath –jar | write classpath as manifest in jar named path |
| classpath -h | print help |
It checks file system integrity
Command prefix “hdfs fcsk “:
| Commands | Description |
|---|---|
| -delete | Delete corrupted files. |
| -files | Print out files being checked. |
| -files -blocks | Print out the block report |
| -files -blocks -locations | Print out locations for every block. |
| -files -blocks -racks | Print out network topology for data-node locations. |
| -files -blocks -replicaDetails | Print out each replica details. |
| -files -blocks -upgradedomains | Print out upgrade domains for every block. |
| -includeSnapshots | Include snapshot data if the given path indicates a snapshottable directory or there are snapshottable directories under it. |
| -list-corruptfileblocks | Print out list of missing blocks and files they belong to. |
| -move | Move corrupted files to /lost+found. |
| -openforwrite | Print out files opened for write. |
| -storagepolicies | Print out storage policy summary for the blocks. |
| -maintenance | Print out maintenance state node details. |
| -blockId | Print out information about the block. |
prefix hdfs getconf:
| Commands | Description |
|---|---|
| -namenodes | gets list of namenodes in the cluster. |
| -secondaryNameNodes | gets list of secondary namenodes in the cluster. |
| -backupNodes | gets list of backup nodes in the cluster. |
| -includeFile | gets the include file path that defines the datanodes that can join the cluster. |
| -excludeFile | gets the exclude file path that defines the datanodes that need to decommissioned. |
| -nnRpcAddresses | gets the namenode rpc addresses |
| -confKey key | gets a specific key from the configuration |
prefix hdfs groups:
| Commands | Description |
|---|---|
| username | Returns the group information given one or more usernames. |
prefix hdfs balancer:
| Commands | Description |
|---|---|
| -policy policy | datanode (default): Cluster is balanced if each datanode is balanced.blockpool: Cluster is balanced if each block pool in each datanode is balanced. |
| -threshold threshold | Percentage of disk capacity. This overwrites the default threshold. |
| -exclude -f hosts-file comma-separated list of hosts | Excludes the specified datanodes from being balanced by the balancer. |
| -include -f comma-separated list of hosts | Includes only the specified datanodes to be balanced by the balancer. |
| -source -f comma-separated list of hosts | Pick only the specified datanodes as source nodes. |
| -blockpools comma-separated list of blockpool ids | The balancer will only run on blockpools included in this list. |
| -idleiterations | Maximum number of idle iterations before exit. This overwrites the default idleiterations(5). |
| -runDuringUpgrade | Whether to run the balancer during an ongoing HDFS upgrade. This is usually not desired since it will not affect used space on over-utilized machines. |
| -h | Display the tool usage and help information and exit. |
Generic options
Prefix hdfs :
| Commands | Description |
|---|---|
| -archives comma separated list of archives | Specify comma separated archives to be unarchived on the compute machines. Applies only to job. |
| -conf configuration file | Specify an application configuration file. |
| -D property=value | Use value for given property. |
| -files comma separated list of files | Specify comma separated files to be copied to the map reduce cluster. Applies only to job. |
| -fs file:/// or hdfs://namenode:port | Specify default filesystem URL to use. Overrides ‘fs.defaultFS’ property from configurations. |
| -jt local or resourcemanager:port | Specify a ResourceManager. Applies only to job. |
| -libjars comma seperated list of jars | Specify comma separated jar files to include in the classpath. Applies only to job. |
See more
Cacheadmin
- hdfs cacheadmin [-addDirective -path -pool [-force] [-replication ] [-ttl ]]
- hdfs cacheadmin [-modifyDirective -id [-path ] [-force] [-replication ] [-pool ] [-ttl ]]
- hdfs cacheadmin [-listDirectives [-stats] [-path ] [-pool ] [-id ]]
- hdfs cacheadmin [-removeDirective ]
- hdfs cacheadmin [-removeDirectives -path ]
- hdfs cacheadmin [-addPool [-owner ] [-group ] [-mode ] [-limit ] [-maxTtl ]]
- hdfs cacheadmin [-modifyPool [-owner ] [-group ] [-mode ] [-limit ] [-maxTtl ]]
- hdfs cacheadmin [-removePool ]
- hdfs cacheadmin [-listPools [-stats] []]
- hdfs cacheadmin [-help ]


official site: https://www.npmjs.com/package/meteor git://github.com/meteor/meteor.git Install Meteor: curl https://install.meteor.com | /bin/sh Create a project: meteor create try-meteor Run it: cd try-meteor meteor Deploy it to the world, for free: meteor deploy try-meteor.meteor.com meteorite: sudo npm install -g meteorite


Some longer text here thats wide enough to span on several lines.
Some longer text here thats wide enough to span on several lines.
Some longer text here thats wide enough to span on several lines.
Some longer text here thats wide enough to span on several lines.


UKCloud Limited (“UKC”) and Virtual Infrastructure Group Limited (“VIG”) (together “the Companies”) – in Compulsory Liquidation
On 25 October 2022, the Companies were placed into Liquidation with the Official Receiver appointed as Liquidator and J Robinson and A M Hudson simultaneously appointed as Special Managers to manage the liquidation process on behalf of the Official Receiver.
Further information regarding the Liquidations can be found here: https://www.gov.uk/government/news/virtual-infrastructure-group-limited-and-ukcloud-limited-information-for-creditors-and-interested-parties
Contact details:
For any general queries relating to the Liquidations please email<EMAIL_ADDRESS>For customer related queries please email<EMAIL_ADDRESS>For supplier related queries please email<EMAIL_ADDRESS>Onboarding responsibilities for UKCloud for VMware
Overview
When UKCloud for VMware is initially provisioned (a process sometimes referred to as onboarding), responsibilities are divided between UKCloud and the customer.
This article indicates which activities are the responsibility of UKCloud and which are the responsibility of the customer.
Responsibilities
The following table outlines the boundaries of responsibilities for onboarding UKCloud for VMware.
Key
R = Responsible - The person who performs an activity or does the work
A = Accountable - The person who is ultimately accountable and has Yes/No/Veto
C = Consulted - A person who needs to feedback and contribute to the activity
I = Informed - A person who needs to know of the decision or action
| Onboarding activity | UKCloud | Customer | Notes |
|---|---|---|---|
| Project management | C | R/A | UKCloud project management available as a Professional Service |
| Solution design, incuding HLD/LLDs | C | R/A | UKCloud solution design and documentation available as a Professional Service |
| Systems configuration documentation | R/A | ||
| Provision of customer account | R/A | C | |
| Provide named UKCloud customer success contact | R/A | I | |
| Provide named UKCloud Service Delivery Manager | R/A | I | |
| Creation of customer virtual environments within account | I | R/A | |
| Creation of Global Administrator | R/A | R/C | |
| Creation of administrators | I | R/A | |
| Provide introduction/overview to UKCloud service and Portal | R/A | C | |
| Provide access to customer Knowledge Centre | R/A | I | |
| Application to join government networks | C | R/A | UKCloud can provide guidance where appropriate to join government networks |
| Provision of customer external connectivity to internet/native government networks | R/A | R/C | |
| Ordering external private networks (for example, leased lines into UKCloud DC Campus (Ark)) | C | R/A | Customer should order from a Telco who has presence in Ark; UKCloud will supply information to assist in ordering |
| Installing external private networks into UKCloud DC campus (Ark) | R/C | R/A | Telco will work directly with Ark to install private network |
| Installing cross-connects from private networks into customer environments | R/A | I | Ark provide physical cabling from Telco termination point to UKCloud Cloud location within Ark |
| Configuration of private network presentation to customer environment | R/A | R/C | Customer environment build available as a Professional Service |
| Provision of complementary firewall services (NSX) | R/A | C/I | |
| Provision of Bring Your Own Firewall (BYOF) | R/C | R/A | Customer environment build available as a Professional Service |
| Creation of firewall rules | I | R/A | Customer environment build available as a Professional Service |
| Creation of virtual networks within customer environment | I | R/A | Customer environment build available as a Professional Service |
| Creation of virtual machines within customer environment | I | R/A | Customer environment build available as a Professional Service |
| Make available general VM OS templates (for example, Windows Server) | R/A | I | |
| Upload of VM image templates | C/I | R/A | Customer environment build available as a Professional Service |
| Import of existing data | C/I | R/A | Bulk data transfer services available on demand using Mass Data Transfer |
| Provision of storage volumes for customer environment | R/A | C | |
| Creation of VM-attached storage | I | RA | Customer environment build available as a Professional Service |
| Provision of VM OS update repositories and licencing services | R/A | I | |
| VM base configuration (NTP/licence activation/connect to repositories) | C | R/A | Customer environment build available as a Professional Service |
| VM security configuration (for example, hardening, AV installation) | I | R/A | Customer environment build available as a Professional Service |
| VM advanced configuration (for example, application/DB installation) | I | R/A | Customer environment build available as a Professional Service |
| VM optimisation and troubleshooting | C | R/A | Customer environment build available as a Professional Service |
| Configuration of VM power schedules | I | R/A | Customer environment build available as a Professional Service |
| Install and configure VM Protective Monitoring solution | I | R/A | UKCloud Security Operations Service available on demand |
| IT Health Check / accreditation activities | C | R/A | Customer environment build available as a Professional Service |
| Provision of backup target storage | R/A | I | |
| Enabling backups for specific VMs | I | R/A | Customer environment build available as a Professional Service |
| Provision of Zerto Journaling Protection for disaster recovery | R/A | C | |
| Configuration of Zerto service to replicate virtual machines | I | R/A | Customer environment build available as a Professional Service |
| Provision of physical hosting for co-location | R/A | C | Co-location is available as a Cloud Enablement service |
| Purchase of physical infrastructure required within co-location | C | R/A | UKCloud can resell hardware (for example, firewalls) to a customer if required |
| Installation of physical infrastructure in hosting service | R/A | R/A | Co-location is available as a Cloud Enablement service |
| Escorting for physical infrastructure installation in hosting service | R/A | R/C | Co-location is available as a Cloud Enablement service |
Feedback
If you find a problem with this article, click Improve this Doc to make the change yourself or raise an issue in GitHub. If you have an idea for how we could improve any of our services, send an email to feedback@ukcloud.com.


This co-opts the com.vaadin.addon.jpacontainer because it uses the JPAContainer and it calls some protected methods. These classes are not part of the official JPAContainer and not supported by Vaadin.


{% extends "base_generic.html" %} {% block title %}
Welcome to Iris, a website developed for Prediction!
The library has the following record counts:


Power
Schema Definition
This is the schema definition for the Power Metrics. It represents the properties for Power Consumption and Power Limiting.Properties
| Property | Type | Nullable | Required | Permission | Description |
|---|---|---|---|---|---|
| @odata.context | String | read-only | The OData description of a payload. Format: uri | ||
| @odata.id | String | read-only | The unique identifier for a resource. Format: uri | ||
| @odata.type | String | read-only | The type of a resource. | ||
| Actions { | Object | read-write | The available actions for this resource. | ||
| Oem { } | Object | read-write | The available OEM specific actions for this resource. | ||
| } | |||||
| Description | String | Yes | read-only | Provides a description of this resource and is used for commonality in the schema definitions. | |
| Id | String | GET | read-only | Uniquely identifies the resource within the collection of like resources. | |
| Name | String | GET | read-only | The name of the resource or array element. | |
| Oem { | Object | read-write | This is the manufacturer/provider specific extension moniker used to divide the Oem object into sections. | ||
| EID_47597 { | Object | read-write | RECS|Box Power extension | ||
| LastSensorUpdate | Int64 | Yes | read-only | The timestamp of the last sensor update expressed as milliseconds since the time 00:00:00 UTC on January 1, 1970. | |
| } | |||||
| } | |||||
| PowerControl [ { | Array[Object] | read-write | This is the definition for power control function (power reading/limiting). | ||
| Actions { | Object | read-write | The available actions for this resource. | ||
| Oem { } | Object | read-write | The available OEM specific actions for this resource. | ||
| } | |||||
| MemberId | String | read-only | This is the identifier for the member within the collection. | ||
| Name | String | Yes | read-only | Power Control Function name. | |
| Oem { } | Object | read-write | This is the manufacturer/provider specific extension moniker used to divide the Oem object into sections. | ||
| PowerConsumedWatts | Decimal | Yes | read-only | The actual power being consumed by the chassis. Unit: W Minimum: 0 | |
| PowerMetrics { | Object | read-write | Power readings for this chassis. | ||
| AverageConsumedWatts | Decimal | Yes | read-only | The average power level over the measurement window (the last IntervalInMin minutes). Unit: W Minimum: 0 | |
| IntervalInMin | Int64 | Yes | read-only | The time interval (or window) in which the PowerMetrics are measured over. Unit: min Minimum: 0 | |
| MaxConsumedWatts | Decimal | Yes | read-only | The highest power consumption level that has occured over the measurement window (the last IntervalInMin minutes). Unit: W Minimum: 0 | |
| MinConsumedWatts | Decimal | Yes | read-only | The lowest power consumption level over the measurement window (the last IntervalInMin minutes). Unit: W Minimum: 0 | |
| } | |||||
| } ] | |||||
|<EMAIL_ADDRESS>| Int32 | read-only | The number of items in a collection. | ||
| Voltages [ { | Array[Object] | read-write | This is the definition for voltage sensors. | ||
| Actions { | Object | read-write | The available actions for this resource. | ||
| Oem { } | Object | read-write | The available OEM specific actions for this resource. | ||
| } | |||||
| LowerThresholdCritical | Decimal | Yes | read-only | Below normal range but not yet fatal. Unit: V | |
| LowerThresholdFatal | Decimal | Yes | read-only | Below normal range and is fatal. Unit: V | |
| LowerThresholdNonCritical | Decimal | Yes | read-only | Below normal range. Unit: V | |
| MaxReadingRange | Decimal | Yes | read-only | Maximum value for this Voltage sensor. Unit: V | |
| MemberId | String | read-only | This is the identifier for the member within the collection. | ||
| MinReadingRange | Decimal | Yes | read-only | Minimum value for this Voltage sensor. Unit: V | |
| Name | String | Yes | read-only | Voltage sensor name. | |
| Oem { } | Object | read-write | This is the manufacturer/provider specific extension moniker used to divide the Oem object into sections. | ||
| ReadingVolts | Decimal | Yes | read-only | The present reading of the voltage sensor. Unit: V | |
| SensorNumber | Int64 | Yes | read-only | A numerical identifier to represent the voltage sensor. | |
| Status { | Object | read-write | This type describes the status and health of a resource and its children. | ||
| Health | String | Yes | read-only | This represents the health state of this resource in the absence of its dependent resources. Possible Values:
| |
| HealthRollup | String | Yes | read-only | This represents the overall health state from the view of this resource. Possible Values:
| |
| Oem { } | Object | read-write | Oem extension object. | ||
| State | String | Yes | read-only | This indicates the known state of the resource, such as if it is enabled. Possible Values:
| |
| } | |||||
| UpperThresholdCritical | Decimal | Yes | read-only | Above normal range but not yet fatal. Unit: V | |
| UpperThresholdFatal | Decimal | Yes | read-only | Above normal range and is fatal. Unit: V | |
| UpperThresholdNonCritical | Decimal | Yes | read-only | Above normal range. Unit: V | |
| } ] | |||||
|<EMAIL_ADDRESS>| Int32 | read-only | The number of items in a collection. |
Operations
GET
Request
GET /redfish/v1/Chassis/Chassis1/Power
Content-Type: application/json
Content-Type: application/json
Response
{
<EMAIL_ADDRESS>"uri",
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Power",
<EMAIL_ADDRESS>"String",
"Actions": {
"Oem": { }
}
"Description": "String",
"Id": "String",
"Name": "String",
"Oem": {
"EID_47597": {
"LastSensorUpdate": 0
}
}
"PowerControl": [{
"Actions": {
"Oem": { }
}
"MemberId": "String",
"Name": "String",
"Oem": { },
"PowerConsumedWatts": 0,
"PowerMetrics": {
"AverageConsumedWatts": 0,
"IntervalInMin": 0,
"MaxConsumedWatts": 0,
"MinConsumedWatts": 0
}
}]
<EMAIL_ADDRESS>1,
"Voltages": [{
"Actions": {
"Oem": { }
}
"LowerThresholdCritical": 0,
"LowerThresholdFatal": 0,
"LowerThresholdNonCritical": 0,
"MaxReadingRange": 0,
"MemberId": "String",
"MinReadingRange": 0,
"Name": "String",
"Oem": { },
"ReadingVolts": 0,
"SensorNumber": 0,
"Status": {
"Health": "OK",
"HealthRollup": "OK",
"Oem": { },
"State": "Enabled"
}
"UpperThresholdCritical": 0,
"UpperThresholdFatal": 0,
"UpperThresholdNonCritical": 0
}]
<EMAIL_ADDRESS>1
}
<EMAIL_ADDRESS>"uri",
<EMAIL_ADDRESS>"/redfish/v1/Chassis/Chassis1/Power",
<EMAIL_ADDRESS>"String",
"Actions": {
"Oem": { }
}
"Description": "String",
"Id": "String",
"Name": "String",
"Oem": {
"EID_47597": {
"LastSensorUpdate": 0
}
}
"PowerControl": [{
"Actions": {
"Oem": { }
}
"MemberId": "String",
"Name": "String",
"Oem": { },
"PowerConsumedWatts": 0,
"PowerMetrics": {
"AverageConsumedWatts": 0,
"IntervalInMin": 0,
"MaxConsumedWatts": 0,
"MinConsumedWatts": 0
}
}]
<EMAIL_ADDRESS>1,
"Voltages": [{
"Actions": {
"Oem": { }
}
"LowerThresholdCritical": 0,
"LowerThresholdFatal": 0,
"LowerThresholdNonCritical": 0,
"MaxReadingRange": 0,
"MemberId": "String",
"MinReadingRange": 0,
"Name": "String",
"Oem": { },
"ReadingVolts": 0,
"SensorNumber": 0,
"Status": {
"Health": "OK",
"HealthRollup": "OK",
"Oem": { },
"State": "Enabled"
}
"UpperThresholdCritical": 0,
"UpperThresholdFatal": 0,
"UpperThresholdNonCritical": 0
}]
<EMAIL_ADDRESS>1
}
POST
This operation is not supported on this resource and will result in the following response:
HTTP/1.1 405 Method Not Allowed
PATCH
This operation is not supported on this resource and will result in the following response:
HTTP/1.1 405 Method Not Allowed
DELETE
This operation is not supported on this resource and will result in the following response:
HTTP/1.1 405 Method Not Allowed


A View can be defined as a virtual table that contains rows and columns with fields from one or more table.
A view can be created using the CREATE VIEW statement. We can create a view from a single table or multiple tables.
Syntax:
CREATE VIEW view_name
AS
SELECT column1,column2....
FROM table_name
WHERE condition;
Simple views and complex views
| Feature | simple | complex |
|---|---|---|
| no of tables | one | more |
| function | no | yes |
| DML operation | yes | no |
A View can be deleted using the Drop View statement.
Syntax:
DROP VIEW view_name;
Example:
DROP VIEW Marks View
Create a view named customer_information to display the customer id, customer name, email id and address of the customers.
Solution: create or replace view customer_information
AS
select custid,custname,emailid,address
from customer;
To
fetch: select * from custinformation;
To delete:Drop view custinformation;
Create a view named regular_customer to display the name of regular customer.
Solution: create or replace View regular_customer
AS
select custname
from
customer
wherecusttype='regular';
To fetch: select * from regularcust;
To delete: Drop view regularcust;
Create a view named item_availability to display the retail outlet id, item type, description, price, unit price and quantity
available of the items in retail stock.
Solution: create or replace View item_availability
AS
select roid,itemtype,descr,price,unitprice,qtyavailable
from item inner join retailstock using(itemcode);
To fetch: select * from
itemavailability;
To delete: Drop viewitemavailability;


📱onslyde.com/go/
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Rendering Performance
Speaking now:
Discussion
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20130927
Demo for Ordered List & Unordered List.
- First Item
- Second Item
- Third Item
- First Item
- Second Item
- Third Item
- First Item
- Second Item
- Third Item
- An Item with default list-style disc.
- Sub Item with list-style circle.
- Another Item
- One another Item
- Another Sub Item with list-style square.
The following list is definition list.
- Optimus Prime
- Leader of the Autobots
- Megatron
- Leader of the Deceptcons
The following list is a nested list.
- Autobots
- Optimus Prime
- BumbleBee
- Deceptcons
- Megatron
- SoundWave
- LaserBeak
- Ravage
- Starscream


Enjoying life
About me
Blog Archive
Blog Archive
2015
nginx搭建rtmp协议的直播环境
2D Coordinate spiral matrix
cocos2d-x setup for mac
New beginning


Filters, Blending, Clipping, and Masking
Chapter 19
Figures
- Basic filter effects
- Drop shadows and alpha channels
- Color filter effects
- Brightness, contrast, and saturation filter effects
- SVG filter effects
- Simple alpha channel blending
- Darken, lighten, difference, and exclusion blending
- Multiply, screen, and overlay blending
- Hard- and soft-light blending
- Color dodge and burn blending
- Hue, saturation, luminosity, and color blending
- Three backgrounds blended together
- Blending with color versus transparency
- Blending elements with their backdrops
- Blending in isolation, and not
- Clipped and unclipped paragraphs
- Various clip shapes
- Various clipping boxes
- Fitting an elliptical clip shape to various boxes
- An image clipped with a scaling SVG clip path
- The two shape-filling options
- A simple image mask
- A variety of image masks
- List items, masked and unmasked
- Alpha and luminance mask modes
- Sizing masks
- Repeating masks
- Positioning masks
- Changing the origin box
- Clipping the mask
- Compositing operations
- Subtracted masks
- Compositing masks
- Two masks
- Four kinds of object fitting
- Various scale-down scenarios
- A variety of object positions
- Positioning a covered object


DATA Event
Oneezy
Data Closest
Click Closest (add class)
Click Closest (toggle class)
Hover Closest (add class)
Hover Closest (toggle class)
Data Target
Click target (add class)
Click target (toggle class)
Hover target (add class)
Hover target (toggle class)
Data Auto
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