I am studying blockchain and I am trying to mine the genesis block of an crypto source.
The source I have is an PoS + masternode source. Of course there is PoW in it to mine the first blocks.
So I generated the genesis hash and merkle root. The daemon boots up and everything works. But the moment I use the "setgenerate true" or "getblocktemplate" commands nothing happens. The genesis block can't be mined.
The "getblocktemplate" returns "Out of memory (code -7)"
Debug.log shows:
2019-01-21 16:23:42 ERROR: CheckTransaction() : txout.nValue negative
2019-01-21 16:23:42 ERROR: CheckBlock() : CheckTransaction failed
2019-01-21 16:23:42 CreateNewBlock() : TestBlockValidity failed
2019-01-21 16:23:42 CreateNewBlock: Failed to detect masternode to pay
2019-01-21 16:23:42 CreateNewBlock(): total size 1000
I disabled the masternode enforcement sporks
Is there anyone who experienced something like this or can help me with it?
The genesis block doesn't actually require mining. You can create it as whatever you want as long as it follows the serialisation of your protocol. Genesis blocks tend to follow slightly different rules to normal blocks and so often do not pass validation under normal circumstances.
Here is how we handle the genesis block in our code-base. It has slightly different rules to how we handle other blocks.
All a block needs is a block to point backwards to. So as long as you have some previous hash new blocks should be able to be formed on top of your genesis block.
I suggest you try Bitshares or Steem code and see how the mining goes. You can use the TEST mode in either one to starting creating / mining blocks from the Genesis block.
Related
I get the following error when I add --conf spark.driver.maxResultSize=2050 to my spark-submit command.
17/12/27 18:33:19 ERROR TransportResponseHandler: Still have 1 requests outstanding when connection from /XXX.XX.XXX.XX:36245 is closed
17/12/27 18:33:19 WARN Executor: Issue communicating with driver in heartbeater
org.apache.spark.SparkException: Exception thrown in awaitResult:
at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:205)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:92)
at org.apache.spark.executor.Executor.org$apache$spark$executor$Executor$$reportHeartBeat(Executor.scala:726)
at org.apache.spark.executor.Executor$$anon$2$$anonfun$run$1.apply$mcV$sp(Executor.scala:755)
at org.apache.spark.executor.Executor$$anon$2$$anonfun$run$1.apply(Executor.scala:755)
at org.apache.spark.executor.Executor$$anon$2$$anonfun$run$1.apply(Executor.scala:755)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1954)
at org.apache.spark.executor.Executor$$anon$2.run(Executor.scala:755)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.io.IOException: Connection from /XXX.XX.XXX.XX:36245 closed
at org.apache.spark.network.client.TransportResponseHandler.channelInactive(TransportResponseHandler.java:146)
The reason of adding this configuration was the error:
py4j.protocol.Py4JJavaError: An error occurred while calling o171.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Total size of serialized results of 16 tasks (1048.5 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)
Therefore, I increased maxResultSize to 2.5 Gb, but the Spark job fails anyway (the error shown above).
How to solve this issue?
It seems like the problem is the amount of data you are trying to pull back to to your driver is too large. Most likely you are using the collect method to retrieve all values from a DataFrame/RDD. The driver is a single process and by collecting a DataFrame you are pulling all of that data you had distributed across the cluster back to one node. This defeats the purpose of distributing it! It only makes sense to do this after you have reduced the data down to a manageable amount.
You have two options:
If you really need to work with all that data, then you should keep it out on the executors. Use HDFS and Parquet to save the data in a distributed manner and use Spark methods to work with the data on the cluster instead of trying to collect it all back to one place.
If you really need to get the data back to the driver, you should examine whether you really need ALL of the data or not. If you only need summary statistics then compute that out on the executors before calling collect. Or if you only need the top 100 results, then only collect the top 100.
Update:
There is another reason you can run into this error that is less obvious. Spark will try to send data back the driver beyond just when you explicitly call collect. It will also send back accumulator results for each task if you are using accumulators, data for broadcast joins, and some small status data about each task. If you have LOTS of partitions (20k+ in my experience) you can sometimes see this error. This is a known issue with some improvements made, and more in the works.
The options for getting past if if this is your issue are:
Increase spark.driver.maxResultSize or set it to 0 for unlimited
If broadcast joins are the culprit, you can reduce spark.sql.autoBroadcastJoinThreshold to limit the size of broadcast join data
Reduce the number of partitions
Cause: caused by actions like RDD's collect() that send big chunk of data to the driver
Solution:
set by SparkConf: conf.set("spark.driver.maxResultSize", "4g")
OR
set by spark-defaults.conf: spark.driver.maxResultSize 4g
OR
set when calling spark-submit: --conf spark.driver.maxResultSize=4g
Idle (0 peers), best: #0 (0xed0a…2e72), finalized #0 (0xed0a…2e72), ⬇ 0 ⬆ 0
Idle (0 peers), best: #0 (0xed0a…2e72), finalized #0 (0xed0a…2e72), ⬇ 0 ⬆ 0
I am getting above output if i run (./target/release/substrate --chain=staging) this command in substrate full node.
I also tried to run a private network for staging , the result was same.
In either of the case the network is not producing the blocks.
Can I get any guide how to use staging?
Need to run in production network and I have seen that for production purposes we should use --staging but not --dev and --local. Is this right?
You need to also add a block producer key to your command.
Not exactly sure what the staging chain specification looks like, but something like:
./target/release/substrate --chain=staging --alice
Where we assume Alice is a configured block producer for the chain.
I have a Dataflow job that has been running stable for several months.
The last 3 days or so, I've problems with the job, it's getting stuck after a certain amount of time and the only thing I can do is stop the job and start a new one. This happened after 2, 6 and 24 hours of processing. Here is the latest exception:
java.lang.ExceptionInInitializerError
at org.apache.beam.runners.dataflow.worker.options.StreamingDataflowWorkerOptions$WindmillServerStubFactory.create (StreamingDataflowWorkerOptions.java:183)
at org.apache.beam.runners.dataflow.worker.options.StreamingDataflowWorkerOptions$WindmillServerStubFactory.create (StreamingDataflowWorkerOptions.java:169)
at org.apache.beam.sdk.options.ProxyInvocationHandler.returnDefaultHelper (ProxyInvocationHandler.java:592)
at org.apache.beam.sdk.options.ProxyInvocationHandler.getDefault (ProxyInvocationHandler.java:533)
at org.apache.beam.sdk.options.ProxyInvocationHandler.invoke (ProxyInvocationHandler.java:158)
at com.sun.proxy.$Proxy54.getWindmillServerStub (Unknown Source)
at org.apache.beam.runners.dataflow.worker.StreamingDataflowWorker.<init> (StreamingDataflowWorker.java:677)
at org.apache.beam.runners.dataflow.worker.StreamingDataflowWorker.fromDataflowWorkerHarnessOptions (StreamingDataflowWorker.java:562)
at org.apache.beam.runners.dataflow.worker.StreamingDataflowWorker.main (StreamingDataflowWorker.java:274)
Caused by: java.lang.RuntimeException: Loading windmill_service failed:
at org.apache.beam.runners.dataflow.worker.windmill.WindmillServer.<clinit> (WindmillServer.java:42)
Caused by: java.io.IOException: No space left on device
at sun.nio.ch.FileDispatcherImpl.write0 (Native Method)
at sun.nio.ch.FileDispatcherImpl.write (FileDispatcherImpl.java:60)
at sun.nio.ch.IOUtil.writeFromNativeBuffer (IOUtil.java:93)
at sun.nio.ch.IOUtil.write (IOUtil.java:65)
at sun.nio.ch.FileChannelImpl.write (FileChannelImpl.java:211)
at java.nio.channels.Channels.writeFullyImpl (Channels.java:78)
at java.nio.channels.Channels.writeFully (Channels.java:101)
at java.nio.channels.Channels.access$000 (Channels.java:61)
at java.nio.channels.Channels$1.write (Channels.java:174)
at java.nio.file.Files.copy (Files.java:2909)
at java.nio.file.Files.copy (Files.java:3027)
at org.apache.beam.runners.dataflow.worker.windmill.WindmillServer.<clinit> (WindmillServer.java:39)
Seems like there is no space left on a device, but shouldn't this be managed by Google? Or is this an error in my job somehow?
UPDATE:
The workflow is as follows:
Reading mass data from PubSub (up to 1500/s)
Filter some messages
Keeping session window on key and grouping by it
Sort the data and do calculations
Output the data to another PubSub
You can increase the storage capacity in the parameter of your pipelise. Look at this one diskSizeGb in this page
In addition, more you keep data in memory, more you need memory. It's the case for the windows, if you never close them, or if you allow late data for too long time, you need a lot of memory to keep all these data up.
Tune either your pipeline, or your machine type. Or both!
I am running an Aerospike cluster in Google Cloud. Following the recommendation on this post, I updated to the last version (3.11.1.1) and re-created all servers. In fact, this change cause my 5 servers to operate in a much lower CPU load (it was around 75% load before, now it is on 20%, as show in the graph bellow:
Because of this low load, I decided to reduce the cluster size to 4 servers. When I did this, my application started to receive the following error:
All batch queues are full
I found this discussion about the topic, recommending to change the parameters batch-index-threads and batch-max-unused-buffers with the command
asadm -e "asinfo -v 'set-config:context=service;batch-index-threads=NEW_VALUE'"
I tried many combinations of values (batch-index-threads with 2,4,8,16) and none of them solved the problem, and also changing the batch-index-threads param. Nothing solves my problem. I keep receiving the All batch queues are full error.
Here is my aerospace.conf relevant information:
service {
user root
group root
paxos-single-replica-limit 1 # Number of nodes where the replica count is automatically reduced to 1.
paxos-recovery-policy auto-reset-master
pidfile /var/run/aerospike/asd.pid
service-threads 32
transaction-queues 32
transaction-threads-per-queue 4
batch-index-threads 40
proto-fd-max 15000
batch-max-requests 30000
replication-fire-and-forget true
}
I use 300GB SSD disks on these servers.
A quick note which may or may not pertain to you:
A common mistake we have seen in the past is that developers decide to use 'batch get' as a general purpose 'get' for single and multiple record requests. The single record get will perform better for single record requests.
It's possible that you are being constrained by the network between the clients and servers. Reducing from 5 to 4 nodes reduced the aggregate pipe. In addition, removing a node will start cluster migrations which adds additional network load.
I would look at the batch-max-buffer-per-queue config parameter.
Maximum number of 128KB response buffers allowed in each batch index
queue. If all batch index queues are full, new batch requests are
rejected.
In conjunction with raising this value from the default of 255 you will want to also raise the batch-max-unused-buffers to batch-index-threads x batch-max-buffer-per-queue + 1 (at least). If you do not do that new buffers will be created and destroyed constantly, as the amount of free (unused) buffers is smaller than the ones you're using. The moment the batch response is served the system will strive to trim the buffers down to the max unused number. You will see this reflected in the batch_index_created_buffers metric constantly rising.
Be aware that you need to have enough DRAM for this. For example if you raise the batch-max-buffer-per-queue to 320 you will consume
40 (`batch-index-threads`) x 320 (`batch-max-buffer-per-queue`) x 128K = 1600MB
For the sake of performance the batch-max-unused-buffers should be set to 13000 which will have a max memory consumption of 1625MB (1.59GB) per-node.
Referring to the docs, you can specify the number of concurrent connection when pushing large files to Amazon Web Services s3 using the multipart uploader. While it does say the concurrency defaults to 5, it does not specify a maximum, or whether or not the size of each chunk is derived from the total filesize / concurrency.
I trolled the source code and the comment is pretty much the same as the docs:
Set the concurrency level to use when uploading parts. This affects
how many parts are uploaded in parallel. You must use a local file as
your data source when using a concurrency greater than 1
So my functional build looks like this (the vars are defined by the way, this is just condensed for example):
use Aws\Common\Exception\MultipartUploadException;
use Aws\S3\Model\MultipartUpload\UploadBuilder;
$uploader = UploadBuilder::newInstance()
->setClient($client)
->setSource($file)
->setBucket($bucket)
->setKey($file)
->setConcurrency(30)
->setOption('CacheControl', 'max-age=3600')
->build();
Works great except a 200mb file takes 9 minutes to upload... with 30 concurrent connections? Seems suspicious to me, so I upped concurrency to 100 and the upload time was 8.5 minutes. Such a small difference could just be connection and not code.
So my question is whether or not there's a concurrency maximum, what it is, and if you can specify the size of the chunks or if chunk size is automatically calculated. My goal is to try to get a 500mb file to transfer to AWS s3 within 5 minutes, however I have to optimize that if possible.
Looking through the source code, it looks like 10,000 is the maximum concurrent connections. There is no automatic calculations of chunk sizes based on concurrent connections but you could set those yourself if needed for whatever reason.
I set the chunk size to 10 megs, 20 concurrent connections and it seems to work fine. On a real server I got a 100 meg file to transfer in 23 seconds. Much better than the 3 1/2 to 4 minute it was getting in the dev environments. Interesting, but thems the stats, should anyone else come across this same issue.
This is what my builder ended up being:
$uploader = UploadBuilder::newInstance()
->setClient($client)
->setSource($file)
->setBucket($bicket)
->setKey($file)
->setConcurrency(20)
->setMinPartSize(10485760)
->setOption('CacheControl', 'max-age=3600')
->build();
I may need to up that max cache but as of yet this works acceptably. The key was moving the processor code to the server and not relying on the weakness of my dev environments, no matter how powerful the machine is or high class the internet connection is.
We can abort the process during upload and can halt all the operations and abort the upload at any instance. We can set Concurrency and minimum part size.
$uploader = UploadBuilder::newInstance()
->setClient($client)
->setSource('/path/to/large/file.mov')
->setBucket('mybucket')
->setKey('my-object-key')
->setConcurrency(3)
->setMinPartSize(10485760)
->setOption('CacheControl', 'max-age=3600')
->build();
try {
$uploader->upload();
echo "Upload complete.\n";
} catch (MultipartUploadException $e) {
$uploader->abort();
echo "Upload failed.\n";
}