I am currently using hbase-indexer to index hbase in solr.
When I execute foolowing command to check the indexer,
hbase-indexer$ bin/hbase-indexer list-indexers --zookeeper 127.0.0.1:2181
The result is said that:
myindexer
+ Lifecycle state: ACTIVE
+ Incremental indexing state: SUBSCRIBE_AND_CONSUME
+ Batch indexing state: INACTIVE
+ SEP subscription ID: Indexer_myindexer
+ SEP subscription timestamp: 2017-01-24T13:15:48.614+09:00
+ Connection type: solr
+ Connection params:
+ solr.zk = localhost:2181/solr
+ solr.collection = tagcollect
+ Indexer config:
222 bytes, use -dump to see content
+ Indexer component factory:
com.ngdata.hbaseindexer.conf.DefaultIndexerComponentFactory
+ Additional batch index CLI arguments:
(none)
+ Default additional batch index CLI arguments:
(none)
+ Processes
+ 1 running processes
+ 0 failed processes
I think hbase-indexer works well as shown above, because it is displayed as + 1 running processes.(Prior to this, I've already executed hbase-indexer daemon by the command : ~$ bin/hbase-indexer server )
For test, I've insert data in Hbase through put command and checked the data was inserted.
But, solr qry said following that: (No Record)
I wish your knowledge and experience associated with this to be shared.
Thank you.
{
"responseHeader":{
"zkConnected":true,
"status":0,
"QTime":7,
"params":{
"q":"*:*",
"indent":"on",
"wt":"json",
"_":"1485246329559"}},
"response":{"numFound":0,"start":0,"maxScore":0.0,"docs":[]
}}
We encountered same issue.
As You are saying sever instance has good health, below are reasons which it wont work.
Firstly, If 'Write ahead log'(WAL) is disabled (may be for write performance reasons) then your puts wont create solr documents.
Hbase NRT indexer works on WAL. if its disabled then it wont create solr documents.
Second reason may be mophiline configurations if they are not correct then it wont create solr documents
However, I'd suggest to write a custom mapreduce programs(or spark jobs as well) to index solr documents by reading hbase data (if not Real time, that means when ever your put data in to hbase immeditely it wont reflect, after mapreduce solr indexer runs solr documents will be created)
Related
I have setup elastalert on a server and managed to run a rule to monitor disk usage. It was running ok until I had to rebuild the server. Now when I run the rule I get error below. I can't find a solution on Internet. Any ideas? Thank you in advanced.
ERROR:root:Error running query: RequestError(400, 'search_phase_execution_exception', 'Text fields are not optimised for operations that require per-document field data like aggregations and sorting, so these operations are disabled by default. Please use a keyword field instead. Alternatively, set fielddata=true on [host.name] in order to load field data by uninverting the inverted index. Note that this can use significant memory.')
alert rule:
name: "warning:High Disk Usage - Disk use is over 85% of capacity:warning"
type: metric_aggregation
index: metricbeat-*
metric_agg_key: system.filesystem.used.pct
metric_agg_type: avg
alert_subject: "High Disk Usage"
max_threshold: 0.15
filter:
- term:
metricset.name: filesystem
- term:
system.filesystem.mount_point: "/"
query_key: host.name
I have a few jobs, say one is loading a text file from a google cloud storage bucket to bigquery table, and another one is a scheduled query to copy data from one table to another table with some transformation, I want the second job to depend on the success of the first one, how do we achieve this in bigquery if it is possible to do so at all?
Many thanks.
Best regards,
Right now a developer needs to put together the chain of operations.
It can be done either using Cloud Functions (supports, Node.js, Go, Python) or via Cloud Run container (supports gcloud API, any programming language).
Basically you need to
issue a job
get the job id
poll for the job id
job is finished trigger other steps
If using Cloud Functions
place the file into a dedicated GCS bucket
setup a GCF that monitors that bucket and when a new file is uploaded it will execute a function that imports into GCS - wait until the operations ends
at the end of the GCF you can trigger other functions for next step
another use case with Cloud Functions:
A: a trigger starts the GCF
B: function executes the query (copy data to another table)
C: gets a job id - fires another function with a bit of delay
I: a function gets a jobid
J: polls for job is ready?
K: if not ready, fires himself again with a bit of delay
L: if ready triggers next step - could be a dedicated function or parameterized function
It is possible to address your scenario with either cloud functions(CF) or with a scheduler (airflow). The first approach is event-driven getting your data crunch immediately. With the scheduler, expect data availability delay.
As it has been stated once you submit BigQuery job you get back job ID, that needs to be check till it completes. Then based on the status you can handle on success or failure post actions respectively.
If you were to develop CF, note that there are certain limitations like execution time (max 9min), which you would have to address in case BigQuery job takes more than 9 min to complete. Another challenge with CF is idempotency, making sure that if the same datafile event comes more than once, the processing should not result in data duplicates.
Alternatively, you can consider using some event-driven serverless open source projects like BqTail - Google Cloud Storage BigQuery Loader with post-load transformation.
Here is an example of the bqtail rule.
rule.yaml
When:
Prefix: "/mypath/mysubpath"
Suffix: ".json"
Async: true
Batch:
Window:
DurationInSec: 85
Dest:
Table: bqtail.transactions
Transient:
Dataset: temp
Alias: t
Transform:
charge: (CASE WHEN type_id = 1 THEN t.payment + f.value WHEN type_id = 2 THEN t.payment * (1 + f.value) END)
SideInputs:
- Table: bqtail.fees
Alias: f
'On': t.fee_id = f.id
OnSuccess:
- Action: query
Request:
SQL: SELECT
DATE(timestamp) AS date,
sku_id,
supply_entity_id,
MAX($EventID) AS batch_id,
SUM( payment) payment,
SUM((CASE WHEN type_id = 1 THEN t.payment + f.value WHEN type_id = 2 THEN t.payment * (1 + f.value) END)) charge,
SUM(COALESCE(qty, 1.0)) AS qty
FROM $TempTable t
LEFT JOIN bqtail.fees f ON f.id = t.fee_id
GROUP BY 1, 2, 3
Dest: bqtail.supply_performance
Append: true
OnFailure:
- Action: notify
Request:
Channels:
- "#e2e"
Title: Failed to aggregate data to supply_performance
Message: "$Error"
OnSuccess:
- Action: query
Request:
SQL: SELECT CURRENT_TIMESTAMP() AS timestamp, $EventID AS job_id
Dest: bqtail.supply_performance_batches
Append: true
- Action: delete
You want to use an orchestration tool, especially if you want to set up this tasks as recurring jobs.
We use Google Cloud Composer, which is a managed service based on Airflow, to do workflow orchestration and works great. It comes with automatically retry, monitoring, alerting, and much more.
You might want to give it a try.
Basically you can use Cloud Logging to know almost all kinds of operations in GCP.
BigQuery is no exception. When the query job completed, you can find the corresponding log in the log viewer.
The next question is how to anchor the exact query you want, one way to achieve this is to use labeled query (means attach labels to your query) [1].
For example, you can use below bq command to issue query with foo:bar label
bq query \
--nouse_legacy_sql \
--label foo:bar \
'SELECT COUNT(*) FROM `bigquery-public-data`.samples.shakespeare'
Then, when you go to Logs Viewer and issue below log filter, you will find the exactly log generated by above query.
resource.type="bigquery_resource"
protoPayload.serviceData.jobCompletedEvent.job.jobConfiguration.labels.foo="bar"
The next question is how to emit an event based on this log for the next workload. Then, the Cloud Pub/Sub comes into play.
2 ways to publish an event based on log pattern are:
Log Routers: set Pub/Sub topic as the destination [1]
Log-based Metrics: create alert policy whose notification channel is Pub/Sub [2]
So, the next workload can subscribe to the Pub/Sub topic, and be triggered when the previous query has completed.
Hope this helps ~
[1] https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#jobconfiguration
[2] https://cloud.google.com/logging/docs/routing/overview
[3] https://cloud.google.com/logging/docs/logs-based-metrics
I am running Spark in standalone mode on 2 machines which have these configs
500gb memory, 4 cores, 7.5 RAM
250gb memory, 8 cores, 15 RAM
I have created a master and a slave on 8core machine, giving 7 cores to worker. I have created another slave on 4core machine with 3 worker cores. The UI shows 13.7 and 6.5 G usable RAM for 8core and 4core respectively.
Now on this I have to process an aggregate of user ratings over a period of 15 days. I am trying to do this using Pyspark
This data is stored in hourwise files in day-wise directories in an s3 bucket, every file must be around 100MB eg
s3://some_bucket/2015-04/2015-04-09/data_files_hour1
I am reading the files like this
a = sc.textFile(files, 15).coalesce(7*sc.defaultParallelism) #to restrict partitions
where files is a string of this form 's3://some_bucket/2015-04/2015-04-09/*,s3://some_bucket/2015-04/2015-04-09/*'
Then I do a series of maps and filters and persist the result
a.persist(StorageLevel.MEMORY_ONLY_SER)
Then I need to do a reduceByKey to get an aggregate score over the span of days.
b = a.reduceByKey(lambda x, y: x+y).map(aggregate)
b.persist(StorageLevel.MEMORY_ONLY_SER)
Then I need to make a redis call for the actual terms for the items the user has rated, so I call mapPartitions like this
final_scores = b.mapPartitions(get_tags)
get_tags function creates a redis connection each time of invocation and calls redis and yield a (user, item, rate) tuple
(The redis hash is stored in the 4core)
I have tweaked the settings for SparkConf to be at
conf = (SparkConf().setAppName(APP_NAME).setMaster(master)
.set("spark.executor.memory", "5g")
.set("spark.akka.timeout", "10000")
.set("spark.akka.frameSize", "1000")
.set("spark.task.cpus", "5")
.set("spark.cores.max", "10")
.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
.set("spark.kryoserializer.buffer.max.mb", "10")
.set("spark.shuffle.consolidateFiles", "True")
.set("spark.files.fetchTimeout", "500")
.set("spark.task.maxFailures", "5"))
I run the job with driver-memory of 2g in client mode, since cluster mode doesn't seem to be supported here.
The above process takes a long time for 2 days' of data (around 2.5hours) and completely gives up on 14 days'.
What needs to improve here?
Is this infrastructure insufficient in terms of RAM and cores (This is offline and can take hours, but it has got to finish in 5 hours or so)
Should I increase/decrease the number of partitions?
Redis could be slowing the system, but the number of keys is just too huge to make a one time call.
I am not sure where the task is failing, in reading the files or in reducing.
Should I not use Python given better Spark APIs in Scala, will that help with efficiency as well?
This is the exception trace
Lost task 4.1 in stage 0.0 (TID 11, <node>): java.net.SocketTimeoutException: Read timed out
at java.net.SocketInputStream.socketRead0(Native Method)
at java.net.SocketInputStream.read(SocketInputStream.java:152)
at java.net.SocketInputStream.read(SocketInputStream.java:122)
at sun.security.ssl.InputRecord.readFully(InputRecord.java:442)
at sun.security.ssl.InputRecord.readV3Record(InputRecord.java:554)
at sun.security.ssl.InputRecord.read(InputRecord.java:509)
at sun.security.ssl.SSLSocketImpl.readRecord(SSLSocketImpl.java:934)
at sun.security.ssl.SSLSocketImpl.readDataRecord(SSLSocketImpl.java:891)
at sun.security.ssl.AppInputStream.read(AppInputStream.java:102)
at org.apache.http.impl.io.AbstractSessionInputBuffer.read(AbstractSessionInputBuffer.java:198)
at org.apache.http.impl.io.ContentLengthInputStream.read(ContentLengthInputStream.java:178)
at org.apache.http.impl.io.ContentLengthInputStream.read(ContentLengthInputStream.java:200)
at org.apache.http.impl.io.ContentLengthInputStream.close(ContentLengthInputStream.java:103)
at org.apache.http.conn.BasicManagedEntity.streamClosed(BasicManagedEntity.java:164)
at org.apache.http.conn.EofSensorInputStream.checkClose(EofSensorInputStream.java:227)
at org.apache.http.conn.EofSensorInputStream.close(EofSensorInputStream.java:174)
at org.apache.http.util.EntityUtils.consume(EntityUtils.java:88)
at org.jets3t.service.impl.rest.httpclient.HttpMethodReleaseInputStream.releaseConnection(HttpMethodReleaseInputStream.java:102)
at org.jets3t.service.impl.rest.httpclient.HttpMethodReleaseInputStream.close(HttpMethodReleaseInputStream.java:194)
at org.apache.hadoop.fs.s3native.NativeS3FileSystem$NativeS3FsInputStream.seek(NativeS3FileSystem.java:152)
at org.apache.hadoop.fs.BufferedFSInputStream.seek(BufferedFSInputStream.java:89)
at org.apache.hadoop.fs.FSDataInputStream.seek(FSDataInputStream.java:63)
at org.apache.hadoop.mapred.LineRecordReader.<init>(LineRecordReader.java:126)
at org.apache.hadoop.mapred.TextInputFormat.getRecordReader(TextInputFormat.java:67)
at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:236)
at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:212)
at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:101)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
at org.apache.spark.rdd.CoalescedRDD$$anonfun$compute$1.apply(CoalescedRDD.scala:93)
at org.apache.spark.rdd.CoalescedRDD$$anonfun$compute$1.apply(CoalescedRDD.scala:92)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:405)
at org.apache.spark.api.python.PythonRDD$WriterThread$$anonfun$run$1.apply(PythonRDD.scala:243)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1617)
at org.apache.spark.api.python.PythonRDD$WriterThread.run(PythonRDD.scala:205)
I could really use some help, thanks in advance
Here is what my main code looks like
def main(sc):
f=get_files()
a=sc.textFile(f, 15)
.coalesce(7*sc.defaultParallelism)
.map(lambda line: line.split(","))
.filter(len(line)>0)
.map(lambda line: (line[18], line[2], line[13], line[15])).map(scoring)
.map(lambda line: ((line[0], line[1]), line[2])).persist(StorageLevel.MEMORY_ONLY_SER)
b=a.reduceByKey(lambda x, y: x+y).map(aggregate)
b.persist(StorageLevel.MEMORY_ONLY_SER)
c=taggings.mapPartitions(get_tags)
c.saveAsTextFile("f")
a.unpersist()
b.unpersist()
The get_tags function is
def get_tags(partition):
rh = redis.Redis(host=settings['REDIS_HOST'], port=settings['REDIS_PORT'], db=0)
for element in partition:
user = element[0]
song = element[1]
rating = element[2]
tags = rh.hget(settings['REDIS_HASH'], song)
if tags:
tags = json.loads(tags)
else:
tags = scrape(song, rh)
if tags:
for tag in tags:
yield (user, tag, rating)
The get_files function is as:
def get_files():
paths = get_path_from_dates(DAYS)
base_path = 's3n://acc_key:sec_key#bucket/'
files = list()
for path in paths:
fle = base_path+path+'/file_format.*'
files.append(fle)
return ','.join(files)
The get_path_from_dates(DAYS) is
def get_path_from_dates(last):
days = list()
t = 0
while t <= last:
d = today - timedelta(days=t)
path = d.strftime('%Y-%m')+'/'+d.strftime('%Y-%m-%d')
days.append(path)
t += 1
return days
As a small optimization, I have created two separate tasks, one to read from s3 and get additive sum, second to read transformations from redis. The first tasks has high number of partitions since there are around 2300 files to read. The second one has much lesser number of partitions to prevent redis connection latency, and there is only one file to read which is on the EC2 cluster itself. This is only partial, still looking for suggestions to improve ...
I was in a similar usecase: doing coalesce on a RDD with 300,000+ partitions. The difference is that I was using s3a(SocketTimeoutException from S3AFileSystem.waitAysncCopy). Finally the issue was resolved by setting a larger fs.s3a.connection.timeout(Hadoop's core-site.xml). Hopefully you can get a clue.
Just encountered a similar issue as described in the below article:
Question: Article with similar error description
java.rmi.UnmarshalException: cannot unmarshaling return; nested exception is:
java.rmi.UnexpectedException: Failed to parse descriptor file; nested exception is:
java.rmi.server.ExportException: Failed to export class
I found that the issue described is totally unrelated to any Java update and is rather an issue with the Weblogic bean-cache. It seems to use old compiled versions of classes when updating a deployment. I was hunting a similar issue in a related question (Question: Interface-Implementation-mismatch).
How can I fix this properly to allow proper automatic deployment (with WLST)?
After some feedback from the Oracle community it now works like this:
1) Shutdown the remote Managed Server
2) Delete directory "domains/#MyDomain#/servers/#MyManagedServer#/cache/EJBCompilerCache"
3) Redeploy EAR/application
In WLST (which one would need to automate this) this is quite tricky:
import shutil
servers=cmo.getServers()
domainPath = get('RootDirectory')
for thisServer in servers:
pathToManagedServer = domainPath + "\\servers\\" + thisServer.getName()
print ">Found managed server:" + pathToManagedServer
pathToCacheDir = pathToManagedServer + "\\" + "cache\\EJBCompilerCache"
if(os.path.exists(pathToCacheDir) and os.path.isdir(pathToCacheDir) ):
print ">Found a cache directory that will be deleted:" + pathToCacheDir
# shutil.rmtree(pathToCacheDir)
Note: Be careful when testing this, the path that is returned by "pathToCacheDir" depends on the MBean-context that is currently set. See samples for WLST command "cd()". You should first test the path output with "print domainPath" and later add the "rmtree" python command! (I uncommented the delete command in my sample, so that nobody accidentially deletes an entire domain!)
I am running a Spark job on a two node standalone cluster (v 1.0.1).
Spark execution often gets stuck at the task mapPartitions at Exchange.scala:44.
This happens at the final stage of my job in a call to saveAsTextFile (as I expect from Spark's lazy execution).
It is hard to diagnose the problem because I never experience it in local mode with local IO paths, and occasionally the job on the cluster does complete as expected with the correct output (same output as with local mode).
This seems possibly related to reading from s3 (of a ~170MB file) immediately prior, as I see the following logging in the console:
DEBUG NativeS3FileSystem - getFileStatus returning 'file' for key '[PATH_REMOVED].avro'
INFO FileInputFormat - Total input paths to process : 1
DEBUG FileInputFormat - Total # of splits: 3
...
INFO DAGScheduler - Submitting 3 missing tasks from Stage 32 (MapPartitionsRDD[96] at mapPartitions at Exchange.scala:44)
DEBUG DAGScheduler - New pending tasks: Set(ShuffleMapTask(32, 0), ShuffleMapTask(32, 1), ShuffleMapTask(32, 2))
The last logging I see before the task apparently hangs/gets stuck is:
INFO NativeS3FileSystem: INFO NativeS3FileSystem: Opening key '[PATH_REMOVED].avro' for reading at position '67108864'
Has anyone else experience non-deterministic problems related to reading from s3 in Spark?