I am new to Cassandra and just run a cassandra cluster (version 1.2.8) with 5 nodes, and I have created several keyspaces and tables on there. However, I found all data are stored in one node (in the below output, I have replaced ip addresses by node numbers manually):
Datacenter: 105
==========
Address Rack Status State Load Owns Token
4
node-1 155 Up Normal 249.89 KB 100.00% 0
node-2 155 Up Normal 265.39 KB 0.00% 1
node-3 155 Up Normal 262.31 KB 0.00% 2
node-4 155 Up Normal 98.35 KB 0.00% 3
node-5 155 Up Normal 113.58 KB 0.00% 4
and in their cassandra.yaml files, I use all default settings except cluster_name, initial_token, endpoint_snitch, listen_address, rpc_address, seeds, and internode_compression. Below I list those non-ip address fields I modified:
endpoint_snitch: RackInferringSnitch
rpc_address: 0.0.0.0
seed_provider:
- class_name: org.apache.cassandra.locator.SimpleSeedProvider
parameters:
- seeds: "node-1, node-2"
internode_compression: none
and all nodes using the same seeds.
Can I know where I might do wrong in the config? And please feel free to let me know if any additional information is needed to figure out the problem.
Thank you!
If you are starting with Cassandra 1.2.8 you should try using the vnodes feature. Instead of setting the initial_token, uncomment # num_tokens: 256 in the cassandra.yaml, and leave initial_token blank, or comment it out. Then you don't have to calculate token positions. Each node will randomly assign itself 256 tokens, and your cluster will be mostly balanced (within a few %). Using vnodes will also mean that you don't have to "rebalance" you cluster every time you add or remove nodes.
See this blog post for a full description of vnodes and how they work:
http://www.datastax.com/dev/blog/virtual-nodes-in-cassandra-1-2
Your token assignment is the problem here. An assigned token are used determines the node's position in the ring and the range of data it stores. When you generate tokens the aim is to use up the entire range from 0 to (2^127 - 1). Tokens aren't id's like with mysql cluster where you have to increment them sequentially.
There is a tool on git that can help you calculate the tokens based on the size of your cluster.
Read this article to gain a deeper understanding of the tokens. And if you want to understand the meaning of the numbers that are generated check this article out.
You should provide a replication_factor when creating a keyspace:
CREATE KEYSPACE demodb
WITH REPLICATION = {'class' : 'SimpleStrategy', 'replication_factor': 3};
If you use DESCRIBE KEYSPACE x in cqlsh you'll see what replication_factor is currently set for your keyspace (I assume the answer is 1).
More details here
Related
I want my consumers to process large batches, so I aim to have the consumer listener "awake", say, on 1800mb of data or every 5min, whichever comes first.
Mine is a kafka-springboot application, the topic has 28 partitions, and this is the configuration I explicitly change:
Parameter
Value I set
Default Value
Why I set it this way
fetch.max.bytes
1801mb
50mb
fetch.min.bytes+1mb
fetch.min.bytes
1800mb
1b
desired batch size
fetch.max.wait.ms
5min
500ms
desired cadence
max.partition.fetch.bytes
1801mb
1mb
unbalanced partitions
request.timeout.ms
5min+1sec
30sec
fetch.max.wait.ms + 1sec
max.poll.records
10000
500
1500 found too low
max.poll.interval.ms
5min+1sec
5min
fetch.max.wait.ms + 1sec
Nevertheless, I produce ~2gb of data to the topic, and I see the consumer-listener (a Batch Listener) is called many times per second -- way more than desired rate.
I logged the serialized-size of the ConsumerRecords<?,?> argument, and found that it is never more than 55mb.
This hints that I was not able to set fetch.max.bytes above the default 50mb.
Any idea how I can troubleshoot this?
Edit:
I found this question: Kafka MSK - a configuration of high fetch.max.wait.ms and fetch.min.bytes is behaving unexpectedly
Is it really impossible as stated?
Finally found the cause.
There is a broker fetch.max.bytes setting, and it defaults to 55mb. I only changed the consumer preferences, unaware of the broker-side limit.
see also
The kafka KIP and the actual commit.
I have a big binary data iof ip data about Xmb. Processes use binary do some search algorithm to lookup ip address. I have three method.
1. put in ets. but I suppose every read access will copy big binary to process. :(
2. put in gen_server state. processes use gen_server:call to get address.The short coming concurrency.
3. compile binary into beam. but when I compile get
eheap_alloc: Cannot allocate 1318267840 bytes of memory (of type "heap")
which the best practice of big data share in erlang?
Binaries over 64 bytes in size are stored as reference counted binaries and their data is stored outside the heap of any process. If such a binary is sent to any process, the underlying data is not duplicated. So, if you store such a binary in an ETS table and then access it from various processes, the underlying data will not be copied, only its reference count will be incremented/decremented. I'd suggest going with the ETS table solution.
Here's a demonstration of the memory usage at boot, after inserting a 100MB binary into an ETS table, and after fetching a copy of the binary into the shell process. The memory usage does not change after we have a copy binary stored in the shell process. The same would not be true if it was million character string (list of integers) that we were copying in from ETS or another process.
1> erlang:memory().
[{total,21912472},
{processes,5515456},
{processes_used,5510816},
{system,16397016},
{atom,223561},
{atom_used,219143},
{binary,844872},
{code,4808780},
{ets,301232}]
2> ets:new(foo, [named_table, set]).
foo
3> ets:insert(foo, {foo, binary:copy(<<".">>, 104857600)}).
true
4> erlang:memory().
[{total,127038632},
{processes,5600320},
{processes_used,5599952},
{system,121438312},
{atom,223561},
{atom_used,220445},
{binary,105770576},
{code,4908097},
{ets,308416}]
5> X = ets:lookup(foo, foo).
[{foo,<<"........................................................................................................"...>>}]
6> erlang:memory().
[{total,127511632},
{processes,6082360},
{processes_used,6081992},
{system,121429272},
{atom,223561},
{atom_used,220445},
{binary,105761504},
{code,4908097},
{ets,308416}]
You can find a lot more info about how to efficiently work with binaries in Erlang in the link above.
I'm using Apache Hive.
I created a table in Hive (similar to external table) and loaded data into the same using the LOAD DATA LOCAL INPATH './Desktop/loc1/kv1.csv' OVERWRITE INTO TABLE adih; command.
While I am able to retrieve simple data from the hive table adih (e.g. select * from adih, select c_code from adih limit 1000, etc), Hive gives me errors when I ask for data involving slight computations (e.g. select count(*) from adih, select distinct(c_code) from adih).
The Hive cli output is as shown in the following link -
hive> select distinct add_user from adih;
Query ID = latize_20161031155801_8922630f-0455-426b-aa3a-6507aa0014c6
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=
In order to set a constant number of reducers:
set mapreduce.job.reduces=
Starting Job = job_1477889812097_0006, Tracking URL = http://latize-data1:20005/proxy/application_1477889812097_0006/
Kill Command = /opt/hadoop-2.7.1/bin/hadoop job -kill job_1477889812097_0006
[6]+ Stopped $HIVE_HOME/bin/hive
Hive stops displaying any further logs / actions beyond the last line of "Kill Command"
Not sure where I have gone wrong (many answers on StackOverflow tend to point back to YARN configs (environment config detailed below).
I have the log as well but it contains more than 30000 characters (Stack Overflow limit)
My hadoop environment is configured as follows -
1 Name Node & 1 Data Node. Each has 20 GB of RAM with sufficient ROM. Have allocated 13 GB of RAM for the yarn.scheduler.maximum-allocation-mb and yarn.nodemanager.resource.memory-mb each with the mapreduce.map.memory.mb being set as 4 GB and the mapreduce.reduce.memory.mb being set as 12 GB. Number of reducers is currently set to default (-1). Also, Hive is configured to run with a MySQL DB (rather than Derby).
You should set the appropriate values to the properties show in your trace,
eg: Edit the properties in hive-site.xml
<property>
<name>hive.exec.reducers.bytes.per.reducer</name>
<value>67108864</value></property>
Looks like you have set mapred.reduce.tasks = -1, which makes Hive refer to its config to decide the number of reduce tasks.
You are getting an error as the number of reducers is missing in Hive config.
Try setting it using below command:
Hive> SET mapreduce.job.reduces=XX
As per official documentation: The right number of reduces seems to be 0.95 or 1.75 multiplied by (< no. of nodes > * < no. of maximum containers per node >).
I managed to get Hive and MR to work - increased the memory configurations for all the processes involved:
Increased the RAM allocated to YARN Scheduler and maximum RAM allocated to the YARN Nodemanager (in yarn-site.xml), alongside increasing the RAM allocated to the Mapper and Reducer (in mapred-site.xml).
Also incorporated parts of the answers by #Sathiyan S and #vmorusu - set the hive.exec.reducers.bytes.per.reducer property to 1 GB of data, which directly affects the number of reducers that Hive uses (through application of its heuristic techniques).
Where can I find information such as the latest block, hash difficulty, average block times, difficulty over time, mining pool sizes, etc.?
"status" can mean a few different things:
Real-time data: http://stats.ethdev.com
"historical" data (i.e. "x over time"): https://etherchain.org/statistics/basic
"historical" data alternative: http://etherscan.io/charts
Market cap: http://coinmarketcap.com/currencies/ethereum/
Price: https://poloniex.com/exchange#btc_eth
And, of course, you can get some data straight from the horse's mouth with any of the ethereum clients. Geth, for instance, will spit out information like so:
I1119 05:42:27.850525 3419 blockchain.go:1142] imported 1 block(s) (0 queued 0 ignored) including 2 txs in 10.879276ms. #564372 [cc3c199d / cc3c199d]
I1119 05:42:35.415481 3419 blockchain.go:1142] imported 1 block(s) (0 queued 0 ignored) including 2 txs in 8.48831ms. #564373 [fc24f250 / fc24f250]
This information includes the latest blocks, timestamps, # of txs and # of uncles.
i want to import csv-Files with about 40 million lines into neo4j. For this i try to use the "batchimporter" from https://github.com/jexp/batch-import.
Maybe it's a problem that i provide own IDs. This is the example
nodes.csv
i:id
l:label
315041100 Person
201215100 Person
315041200 Person
rels.csv :
start
end
type
relart
315041100 201215100 HAS_RELATION 30006
315041200 315041100 HAS_RELATION 30006
the content of batch.properties:
use_memory_mapped_buffers=true
neostore.nodestore.db.mapped_memory=1000M
neostore.relationshipstore.db.mapped_memory=5000M
neostore.propertystore.db.mapped_memory=4G
neostore.propertystore.db.strings.mapped_memory=2000M
neostore.propertystore.db.arrays.mapped_memory=1000M
neostore.propertystore.db.index.keys.mapped_memory=1500M
neostore.propertystore.db.index.mapped_memory=1500M
batch_import.node_index.node_auto_index=exact
./import.sh graph.db nodes.csv rels.csv
will be processed without errors, but it takes about 60 seconds!
Importing 3 Nodes took 0 seconds
Importing 2 Relationships took 0 seconds
Total import time: 54 seconds
When i use smaller IDs - for example 3150411 instead of 315041100 - it takes just 1 second!
Importing 3 Nodes took 0 seconds
Importing 2 Relationships took 0 seconds
Total import time: 1 seconds
Actually i would take even bigger IDs with 10 digits. I don't know what i'm doing wrong. Can anyone see an error?
JDK 1.7
batchimporter 2.1.3 (with neo4j 2.1.3)
OS: ubuntu 14.04
Hardware: 8-Core-Intel-CPU, 16GB RAM
I think the problem is that the batch importer is interpreting those IDs as actual physical ids on disk. And so the time is spent in the file system, inflating the store files up to the size where they can fit those high ids.
The ids that you're giving are intended to be "internal" to the batch import, or? Although I'm not sure how to tell the batch importer that is the case.
#michael-hunger any input there?
the problem is that those ID's are internal to Neo4j where they represent disk record-ids. if you provide high values there, Neo4j will create a lot of empty records until it reaches your ids.
So either you create your node-id's starting from 0 and you store your id as normal node property.
Or you don't provide node-id's at all and only lookup nodes via their "business-id-value"
i:id id:long l:label
0 315041100 Person
1 201215100 Person
2 315041200 Person
start:id end:id type relart
0 1 HAS_RELATION 30006
2 0 HAS_RELATION 30006
or you have to configure and use an index:
id:long:people l:label
315041100 Person
201215100 Person
315041200 Person
id:long:people id:long:people type relart
0 1 HAS_RELATION 30006
2 0 HAS_RELATION 30006
HTH Michael
Alternatively you can also just write a small java or groovy program to import your data if handling those ids with the batch-importer is too tricky.
See: http://jexp.de/blog/2014/10/flexible-neo4j-batch-import-with-groovy/