I was trying to import a CSV with about 20 million rows.
I did a pilot run with a few 100 rows worth of CSV just to check if the columns were in order and that there were no parsing errors. All went well.
Every time I tried importing the 20 million row CSV, it failed after varying amounts of time. On my local machine it failed after 90 minutes with the following error. On the server box it fails within 10 minutes:
Processed 4050000 rows; Write: 624.27 rows/ss
code=1100 [Coordinator node timed out waiting for replica nodes' responses] message="Operation timed out - received only 0 responses." info=
{'received_responses': 0, 'required_responses': 1, 'write_type': 0, 'consistency': 1}
Aborting import at record #4050617. Previously-inserted values still present.
4050671 rows imported in 1 hour, 26 minutes, and 43.649 seconds.
Cassandra: Coordinator node timed out waiting for replica nodes' responses (It is a one node cluster and replication factor is 1 so why is it wating for other nodes is another question)
Then based on recommendation in another thread I changed the write time out though I was not convinced it was the root cause.
write_request_timeout_in_ms: 20000
(Also tried changing it to 300000)
But it still eventually fails.
So now, I have chopped the original CSV into many 500,000 line CSVs.
This has a better success rate (compared to 0!). But even this fails 2 of 5 times for various reasons.
Sometimes I get the following error:
Processed 460000 rows; Write: 6060.32 rows/ss
Connection heartbeat failure
Aborting import at record #443491. Previously inserted records are still present, and some records after that may be present as well.
Other times it just stops updating the progress on console and the only way out is to abort using Ctrl+C
I've spent most of the day like this. My RDBMS is running happily with 5 billion rows. I wanted to test Cassandra with 10 times as much data but I'm having trouble even importing a million rows at a time.
One observation about how the COPY command proceeds is this: Once the command is entered, it starts writing at the rate of about 10,000 rows per second. It sustanins this speed till it has inserted about 80,000 rows. Then there is a pause of about 30 seconds after which it consumes another 70,000 to 90,000 rows, pauses for another 30 seconds and so on till it either finishes all rows in the CSV or fails midway with an error or simply hangs.
I need to get to the root of this. I really hope to find that I am doing something silly and it's not something I have to accept and work around.
I am using Cassandra 2.2.3
There is a lot of people having trouble with the COPY command, it seems that it works for small datasets but it starts to fail when you have a lot of data.
In the documentation they recommend to use the SSTable loader if you have a few million rows to import, i used it with my company and I had a lot of consistency problems.
I have tried everything and for me the safest way to import large amount of data into cassandra is by writing a little script that reads your CSV and then execute async queries. Python does it very well.
Will is correct. COPY is meant for small data sets and usually struggles when you start hitting the millions of rows. In addition to SSTable loader, there's this utility: https://github.com/brianmhess/cassandra-loader which I find to have very good performance with some added convenience.
Related
What we are doing is pretty much like
putting time series data into cassandra
running an spark aggregation job every hour and put aggregated data back to cassandra
One of the problems we found is, if the hourly job does not succeed, for example, continuously, 1 AM ~ 2 AM, 2 AM ~ 3 AM, 3 AM ~ 4 AM (or more), then next time, it'll aggregate the data from 1 AM to 5 AM (last success time is recorded in cassandra). The issue comes at this hour, because it's now 4 (or more) hours data, and it's way larger than one hour data which then results in an OutofMemory exception by selecting too many data from cassandra into dataframe.
Well, adding memory to spark executor is a way fixing this. However, considering it's an edge issue, I'm wondering if there's any mature pattern or architecture to deal with this issue.
I am having issues with doing counts on a single table with up to 1million records. I have a 32 core 244gb ram box that I am running my test on so hardware should not be an issue.
I have indexes set up on all of my queries that I am using to perform counts. I have enabled node max_old_space_size to 15gb.
The process I am following is basically looping through a huge array, creating 1000 promises, within each promise I am performing 12 counts, waiting for the promises to all resolve, and then continuing with the next one thousand batch.
As part of my test, I am doing inserts, updates, and reads as well. All of those, are showing great performance up to 20000/sec on each. However, when I get to the portion of my code doing the counts(), I can see via mongostat that there are only 20-30 commands being executed per second. I have not determined at this point, if my node code is only sending that many, or if mongo is queuing it up.
Meanwhile, in my node.js code, all 1000 promises are started and waiting to evaluate. I know this is a lot of info, so please let me know what more granular details I should provide to get some more insight into why the count performance is so slow.
So basically, for a batch of 1000 records, doing lets say 12 counts each, for a total of 12,000 counts, it is taking close to 10 minutes, on a table of 1million records.
MongoDB Native Client v2.2.1
Node v4.2.1
What I'd like to add is that I have tried changing the maxPoolSize on the driver from 100-1000 with no change in performance. I've tried changing my queries that I perform from yield/generator/promise to callbacks wrapped in promise, which has helped somewhat.
The strange thing is, when my program starts, even if i use just the default number of connections which I see as 7 when running mongostat, I can get around 2500 count() queries per second throughout. However, after a few seconds this goes back down to about 300-400. This leads me to believe that mongo can handle that many all the time, but my code is not able to send that many requests, even though I set maxPoolSize to 300 and start 10000 simultaneous promises resolving in parallel. So what gives, any ideas from anyone ?
Note
This is getting quite long so I will try and re-edit parts through the day.
These databases are no long active, which means I can play with them to work out what is going wrong.
The only thing left to answer: Given two databases running on Azure Databases at S3 (100 DTU). Should any secondary ever get significantly behind the primary database? Even while the DTU is hammered to 100% for over half the day. The reason for the DTU being hammered being IO writes mostly.
The Start: a few problems.
DTU limits were hit on Monday, Tuesday and to some extent on Wednesday for a significant amount of time. 3PM UTC - 6AM UTC.
Problem 1 (lag in data on the secondary): This had appeared to have caused a lag of data in the secondary of about 9 1/2 hours. The servers were effectively being spammed with updates causing a lot of IO updates. 6-8 million records on one table for the 24 hour period for example. This problem drove the reason for the post:
Shouldn't these be much more in sync?
The data became out of sync on Monday morning and continued out of sync until Friday. On Thursday some new databases were started up to replace these standard SQL databases and so they were left to rot. Well, for me to experiment with at least.
The application causing the redundant queries couldn't be fixed for a few days (I'm doubting they will ever fix it now) so that leaves changing the instance type. That action was attempted on the current instance but, the instance must disconnect with all standard replicas to increase to the performance tier. This led to the second problem (see below). The replica taking its time to be removed. Began on Wednesday morning and did not complete until Friday.
Problem 2 (can't remove the replica):
(Solved itself after two days)
Disconnecting the secondary database process began ~ Wed 8UTC (when the primary was at about 80GBs). The secondary being about 11GB behind in size at this point.
Setup
The databases (primary and secondary) are S3 that is geo-replicated (North + West Europe).
It has an audit log table(which I read from the secondary - normally with an SQL query), but this is currently 9 1/2 hours behind the last entry for the primary database. Running the query again on the secondary a few seconds later it is slowly catching up, but appears to be relative to the refresh rather than playing catch-up.
Both primary and secondary (read-only) databases are S3 (about to be bumped to P2).
the azure documentation states:
Active Geo-Replication (readable secondaries) is now available for all databases in all service tiers. In April 2017, the non-readable secondary type will be retired and existing non-readable databases will automatically be upgraded to readable secondaries.
How has the secondary has got so far behind? seconds to minutes would be acceptable. Hours not so much. The link above describes it as slightly:
While at any given point, the secondary database might be slightly behind the primary database, the secondary data is guaranteed to always be transactionally consistent with changes committed to the primary database.
Given the secondary is about to be destroyed and replaced by a higher level (need to remove replicas when upgrading from standard -> premium). I'm curious to know if it will happen again as well as what the definition of slight might be in this instance?
Notes: The primary did reach maximum DTU for a few hours but didn't harm the performance significantly, which is where the 9-hour difference was noticed.
Stats
Update for TheGameiswar:
I can't query it right now as it started removing itself as a replica (to be able to move the primary up to the P2 level, but that began hours ago at ~8.30UTC and 5 hours later it is still going). I think it's quite broken now.
Query - nothing special:
SELECT TOP 1000 [ID]
,[DateCreated]
,[SrcID]
,[HardwareID]
,[IP_Port]
,[Action]
,[ResponseTime]
,[ErrorCode]
,[ExtraInfo]
FROM [dbo].[Audit]
order by datecreated desc
I can't compare the tables anymore as it's quite stuck and refusing connections.
The 586 hours (10-14GB) are inserts into the primary database audit table. It was similar yesterday when noticing the 9 1/2 hour difference in data.
When the attempt to remove the replica (another person stated the process) it had about 10GB difference in size.
Cant compare data but can show DB-size at equivalent time
Primary DB Size (last 24 hours):
Secondary DB Size (last 24 hours):
Primary database size - week view
Secondary database size - week view
As mentioned ... it is being removed as a replica... but is still playing catch up with the DB size if you observe the charts above.
Stop replication errored for serverName: ---------, databaseName: Cloud4
ErrorCode: 400
ErrorMessage: A terminate operation is already in progress for database 'Cloud4'.
Update 2 - Replication - dm_continuous_copy_status
Replication is still removing ... moving on...
select * from sys.dm_continuous_copy_status
sys.dm_exec_requests
Querying from Thursday
Appears to be quite empty. The only record being
Replica removed itself at last.
The replica has removed itself after 2 days. At the 80GB mark that I predicted. It waited to replay the data in the transactions (till the point it was removed as a replica) before it would remove the replica.
A Week after on the P2 databases
DTU is holding between 20-40% at busy periods and currently performing ~12 million data entries every 24 hours (a similar amount for reads, but writing is much worse on the indexes and the table). 70-100% inserts extra in a week. This time, the replica is not struggling to keep up, which is good but that is likely due to it not reaching 100% DTU.
Conclusion
The replicas are useful but not in this case. This one caused degraded performance for several days that could have been averted. A simple increase to the performance tier until the cause of the problem could be fixed. IF the replica looks like it is dragging behind and you are on the border of Basic -> Standard or Standard -> Performance it would be safe to remove the replica as soon as possible and increase to a different tier.
Now we are on P2. The database is increasing at 20GB a day... and they say they have fixed the problem that sends 15 thousand redundant updates per minute. Thanks to the query performance insight for highlight that as querying the table is extremely painful on the DTU (even querying the last minute of data in that table is bad on the DTU. ~15 thousand new records every minute).
62617: insert ...
62618: select ...
62619: select ...
A positive from the above is that it's moved from 586 hours combined time for the insert statements (7.5 million entry rows per day) on S3 to 3 hours on P2 (12.4 million row rows per day). An extremely significant decrease in processing time. It did start with an empty table on Thursday but that has surpassed the previous size in a week whereas the previous one took a few months to get there.
It's doing well on the new tier. It should be ~5% if the applications were using the database responsibly and the secondary is up to date.
Spoke too soon. Now on P2
Someone thought it was a good idea to run an SQL query that repeats itself that deletes a thousand rows at a time. 12 million new rows a day.
10AM - 12AM it's managed to remove about 5.2 million rows. Now the database is showing signs of being in the same state as last week. Im curious if that is what happened now.
I have 5 nodes in my ring with SimpleTopologyStrategy and replication_factor=3. I inserted 1M rows using stress tool . When am trying to read the row count in cqlsh using
SELECT count(*) FROM Keyspace1.Standard1 limit 1000000;
It fails with error:
Request did not complete within rpc_timeout.
It fetches for limit 100000. Fails even for 500000.
All my nodes are up. Do I need to increase the rpc_timeout?
Please help.
You get this error because the request is timing out on the server side. One should know that this is a very expensive operation in Cassandra as others have pointed out.
Still, if you really want to do this you should update your /etc/cassandra/cassandra.yaml file and change the range_request_timeout_in_ms parameter. This will be valid for all your range queries.
Example to set a 40 second timeout:
range_request_timeout_in_ms: 40000
You will probably have to adjust at the client side as well. When using cqlsh as a client this is accomplished by creating/updating your configuration file for cqlsh under ~/.cassandra/cqlshrc and add the client_timeout parameter to the connection section.
Example to set a 40 second timeout:
[connection]
client_timeout=40
It takes a long time to read in 1M rows so that is probably why it is timing out. You shouldn't use count like this, it is very expensive since it has to read all the data. Use Cassandra counters if you need to count lots of items.
You should also check your Cassandra logs to confirm there aren't any other issues - sometimes exceptions in Cassandra lead to timeouts on the client.
If you can live with an approximate row count, take a look at this answer to Row count of a column family in Cassandra.
Say I have about 150 requests coming in every second to an api (node.js) which are then logged in Redis. At that rate, the moderately priced RedisToGo instance will fill up every hour or so.
The logs are only necessary to generate daily\monthly\annual statistics: which was the top requested keyword, which was the top requested url, total number of requests daily, etc. No super heavy calculations, but a somewhat time-consuming run through arrays to see which is the most frequent element in each.
If I analyze and then dump this data (with a setInterval function in node maybe?), say, every 30 minutes, it doesn't seem like such a big deal. But what if all of sudden I have to deal with, say, 2500 requests per second?
All of a sudden I'm dealing with 4.5 ~Gb of data per hour. About 2.25Gb every 30 minutes. Even with how fast redis\node are, it'd still take a minute to calculate the most frequent requests.
Questions:
What will happen to the redis instance while 2.25 gb worth of dada is being processed? (from a list, I imagine)
Is there a better way to deal with potentially large amounts of log data than moving it to redis and then flushing it out periodically?
IMO, you should not use Redis as a buffer to store your log lines and process them in batch afterwards. It does not really make sense to consume memory for this. You will better served by collecting your logs in a single server and write them on a filesystem.
Now what you can do with Redis is trying to calculate your statistics in real-time. This is where Redis really shines. Instead of keeping the raw data in Redis (to be processed in batch later), you can directly store and aggregate the statistics you need to calculate.
For instance, for each log line, you could pipeline the following commands to Redis:
zincrby day:top:keyword 1 my_keyword
zincrby day:top:url 1 my_url
incr day:nb_req
This will calculate the top keywords, top urls and number of requests for the current day. At the end of the day:
# Save data and reset counters (atomically)
multi
rename day:top:keyword tmp:top:keyword
rename day:top:url tmp:top:url
rename day:nb_req tmp:nb_req
exec
# Keep only the 100 top keyword and url of the day
zremrangebyrank tmp:top:keyword 0 -101
zremrangebyrank tmp:top:url 0 -101
# Aggregate monthly statistics for keyword
multi
rename month:top:keyword tmp
zunionstore month:top:keyword 2 tmp tmp:top:keyword
del tmp tmp:top:keyword
exec
# Aggregate monthly statistics for url
multi
rename month:top:url tmp
zunionstore month:top:url 2 tmp tmp:top:url
del tmp tmp:top:url
exec
# Aggregate number of requests of the month
get tmp:nb_req
incr month:nb_req <result of the previous command>
del tmp:nb_req
At the end of the month, the process is completely similar (using zunionstore or get/incr on monthly data to aggregate the yearly data).
The main benefit of this approach is the number of operations done for each log line is limited while the monthly and yearly aggregation can easily be calculated.
how about using flume or chukwa (or perhaps even scribe) to move log data to a different server (if available) - you could store log data using hadoop/hbase or any other disk based store.
https://cwiki.apache.org/FLUME/
http://incubator.apache.org/chukwa/
https://github.com/facebook/scribe/