Sometimes time bucketing can be used to create manageable partition sizes. How
much data is attached to a day, week, or minute? Could you use a partition and
clustering key like: ((source, time_bucket), timestamp)?
Then your application logic can iterate through time buckets to pull out the
data in scalable chunks:
Select column1, column2 from my_table where source = ‘PRIME SOURCE’ and
time_bucket = ‘2021-03-15’;
Select column1, column2 from my_table where source = ‘PRIME SOURCE’ and
time_bucket = ‘2021-03-16’
…
Also, there are implementations of Spark that will create the proper, single
partition queries for large data sets. DataStax Analytics is one example (spark
runs on each node).
Sean Durity – Staff Systems Engineer, Cassandra
From: Bowen Song <bo...@bso.ng.INVALID>
Sent: Monday, March 15, 2021 5:27 PM
To: user@cassandra.apache.org
Subject: [EXTERNAL] Re: No node was available to execute query error
There are different approaches, depending on the application's logic. Roughly
speaking, there's two distinct scenarios:
1. Your application knows all the partition keys of the required data in
advance, either by reading them from another data source (e.g.: another
Cassandra table, other database, a file, or an API), or can reconstruct the
partition keys from other known information (e.g.: sequential numbers, date
time in a known range, etc.).
2. Your application needs all (or nearly all) rows from a given table, so
you can use range requests to read everything out from that table.
However, before you choose the second option and create a table for each
"source" value, I must warn you that creating hundreds of tables in Cassandra
is a bad idea.
Ask yourself a question, what is really required to 'do something'? Do you
really need all data each time? Is it possible to make 'do something'
incremental, so you'll only need some data each time?
On 15/03/2021 19:33, Joe Obernberger wrote:
Thank you.
What is the best way to iterate over a very large number of rows in Cassandra?
I know the datastax driver let's java do blocks of n records, but is that the
best way?
-joe
On 3/15/2021 1:42 PM, Bowen Song wrote:
I personally try to avoid using secondary indexes, especially in large clusters.
SI is not scalable, because a SI query doesn't have the partition key
information, Cassandra must send it to nearly all nodes in a DC to get the
answer. Thus, the more nodes you have in a cluster, the slower and more
expensive to run a SI query. Creating a SI on a table also can indirectly
create large partitions in the index tables.
On 15/03/2021 17:27, Joe Obernberger wrote:
Great stuff - thank you. I've spent the morning here redesigning with smaller
partitions.
If I have a large number of unique IDs that I want to regularly 'do something'
with, would it make sense to have a table where a UUID is the partition key,
and create a secondary index on a field (call it source) that I want to select
from where the number of UUIDs per source might be very large (billions).
So - select * from table where source=?
The number of unique source values is small - maybe 1000
Whereas each source may have billions of UUIDs.
-Joe
On 3/15/2021 11:18 AM, Bowen Song wrote:
To be clear, this
CREATE TABLE ... PRIMARY KEY (k1, k2);
is the same as:
CREATE TABLE ... PRIMARY KEY ((k1), k2);
but they are NOT the same as:
CREATE TABLE ... PRIMARY KEY ((k1, k2));
The first two statements creates a table with a partition key k1 and a
clustering key k2. The 3rd statement creates a composite partition key from k1
and k2, therefore k1 and k2 are the partition keys for this table.
Your example "create table xyz (uuid text, source text, primary key (source,
uuid));" uses the same syntax as the first statement, which creates the table
xyz with a partition key source, and a clustering key uuid (which, BTW, is a
non-reserved keyword).
A partition in Cassandra is solely determined by the partition key(s), and the
clustering key(s) have nothing to do with it. The size of a compacted partition
is determined by the number of rows in the partition and the size of each row.
If the table doesn't have a clustering key, each partition will have at most
one row. The row size is the serialized size of all data in that row, including
tombstones.
You can reduce the partition size for a table by either reducing the serialized
data size or adding more columns to the (composite) partition keys. But please
be aware, you will have to provide ALL partition key values when you read from
or write to this table (other than range, SI or MV queries), therefore you will
need to consider the queries before designing the table schema. For
scalability, you will need predictable partition size that does not grow over
time, or have an actionable plan to re-partition the table when the partition
size exceeds a certain threshold. Picking the threshold is more of an art than
science, generally speaking it should stay below a few hundred MBs, and often
no more than 100 MB.
On 15/03/2021 14:36, Joe Obernberger wrote:
Thank you Bowen - I'm redesigning the tables now. When you give Cassandra two
parts to the primary key like
create table xyz (uuid text, source text, primary key (source, uuid));
How is the second part of the primary key used to determine partition size?
-Joe
On 3/12/2021 5:27 PM, Bowen Song wrote:
The partition size min/avg/max of 8409008/15096925/25109160 bytes looks fine
for the table fieldcounts, but the number of partitions is a bit worrying. Only
3 partitions? Are you expecting the partition size (instead of number of
partitions) to grow in the future? That can lead to a lots of headaches.
Forget about the fieldcounts table for now, the doc table looks really bad. It
has min/avg/max partition size of 24602/7052951452/63771372175 bytes, the
partition sizes are severely unevenly distributed, and the over 60GB partition
is way too big.
You really need to redesign your table schemas, and avoid creating large or
uneven partitions.
On 12/03/2021 18:52, Joe Obernberger wrote:
Thank you very much for helping me out on this! The table fieldcounts is
currently pretty small - 6.4 million rows.
cfstats are:
Total number of tables: 81
----------------
Keyspace : doc
Read Count: 3713134
Read Latency: 0.2664131157130338 ms
Write Count: 47513045
Write Latency: 1.0725477948634947 ms
Pending Flushes: 0
Table: fieldcounts
SSTable count: 3
Space used (live): 16010248
Space used (total): 16010248
Space used by snapshots (total): 0
Off heap memory used (total): 4947
SSTable Compression Ratio: 0.3994304032360534
Number of partitions (estimate): 3
Memtable cell count: 0
Memtable data size: 0
Memtable off heap memory used: 0
Memtable switch count: 0
Local read count: 379
Local read latency: NaN ms
Local write count: 0
Local write latency: NaN ms
Pending flushes: 0
Percent repaired: 100.0
Bloom filter false positives: 0
Bloom filter false ratio: 0.00000
Bloom filter space used: 48
Bloom filter off heap memory used: 24
Index summary off heap memory used: 51
Compression metadata off heap memory used: 4872
Compacted partition minimum bytes: 8409008
Compacted partition maximum bytes: 25109160
Compacted partition mean bytes: 15096925
Average live cells per slice (last five minutes): NaN
Maximum live cells per slice (last five minutes): 0
Average tombstones per slice (last five minutes): NaN
Maximum tombstones per slice (last five minutes): 0
Dropped Mutations: 0
Commitlog is on a separate spindle on the 7 node cluster. All disks are SATA
(spinning rust as they say!). This is an R&D platform, but I will switch to
NetworkTopologyStrategy. I'm using Prometheus and Grafana to monitor Cassandra
and the CPU load is typically 100 to 200% on most of the nodes. Disk IO is
typically pretty low.
Performance - in general Async is about 10x faster.
ExecuteAsync:
35mSec for 364 rows.
8120mSec for 205001 rows.
14788mSec for 345001 rows.
4117mSec for 86400 rows.
23,330 rows per second on average
Execute:
232mSec for 364 rows.
584869mSec for 1263283 rows
46290mSec for 86400 rows
2,160 rows per second on average
Curious - our largest table (doc) has the following stats - is it not
partitioned well?
Total number of tables: 81
----------------
Keyspace : doc
Read Count: 3713134
Read Latency: 0.2664131157130338 ms
Write Count: 47513045
Write Latency: 1.0725477948634947 ms
Pending Flushes: 0
Table: doc
SSTable count: 26
Space used (live): 57124641753
Space used (total): 57124641753
Space used by snapshots (total): 113012646218
Off heap memory used (total): 27331913
SSTable Compression Ratio: 0.2531585373184219
Number of partitions (estimate): 12
Memtable cell count: 0
Memtable data size: 0
Memtable off heap memory used: 0
Memtable switch count: 0
Local read count: 27169
Local read latency: NaN ms
Local write count: 0
Local write latency: NaN ms
Pending flushes: 0
Percent repaired: 0.0
Bloom filter false positives: 0
Bloom filter false ratio: 0.00000
Bloom filter space used: 576
Bloom filter off heap memory used: 368
Index summary off heap memory used: 425
Compression metadata off heap memory used: 27331120
Compacted partition minimum bytes: 24602
Compacted partition maximum bytes: 63771372175
Compacted partition mean bytes: 7052951452
Average live cells per slice (last five minutes): NaN
Maximum live cells per slice (last five minutes): 0
Average tombstones per slice (last five minutes): NaN
Maximum tombstones per slice (last five minutes): 0
Dropped Mutations: 0
Thank again!
-Joe
On 3/12/2021 11:01 AM, Bowen Song wrote:
Sleep-then-retry works is just another indicator that it's likely a GC pause
related issue. I'd recommend you to check your Cassandra servers' GC logs first.
Do you know what's the maximum partition size for the doc.fieldcounts table?
(Try the "nodetool cfstats doc.fieldcounts" command) I suspect this table has
large partitions, which usually leads to GC issues.
As of your failed executeAsync() insert issue, do you know how many concurrent
on-the-fly queries do you have? Cassandra driver has limitations on it, and new
executeAsync() calls will fail when the limit is reached.
I'm also a bit concerned about your "significantly" slower inserts. Inserts
(excluding "INSERT IF NOT EXISTS") should be very fast in Cassandra. How slow
are they? Are they always slow like that, or usually fast but some are much
slower than others? What does the CPU usage & disk IO look like on the
Cassandra server? Do you have commitlog on the same disk as the data? Is it a
spinning disk, SATA SSD or NVMe?
BTW, you really shouldn't use SimpleStrategy for production environments.
On 12/03/2021 15:18, Joe Obernberger wrote:
The queries that are failing are:
select fieldvalue, count from doc.ordered_fieldcounts where source=? and
fieldname=? limit 10
Created with:
CREATE TABLE doc.ordered_fieldcounts (
source text,
fieldname text,
count bigint,
fieldvalue text,
PRIMARY KEY ((source, fieldname), count, fieldvalue)
) WITH CLUSTERING ORDER BY (count DESC, fieldvalue ASC)
and:
select fieldvalue, count from doc.fieldcounts where source=? and fieldname=?
Created with:
CREATE TABLE doc.fieldcounts (
source text,
fieldname text,
fieldvalue text,
count bigint,
PRIMARY KEY (source, fieldname, fieldvalue)
)
This really seems like a driver issue. I put retry logic around the calls and
now those queries work. Basically if it throws an exception, I
Thread.sleep(500) and then retry. This seems to be a continuing theme with
Cassandra in general. Is this common practice?
After doing this retry logic, an insert statement started failing with an
illegal state exception when I retried it (which makes sense). This insert was
using session.executeAsync(boundStatement). I changed that to just execute
(instead of async) and now I get no errors, no retries anywhere. The insert is
*significantly* slower when running execute vs executeAsync. When using
executeAsync:
com.datastax.oss.driver.api.core.NoNodeAvailableException: No node was
available to execute the query
at
com.datastax.oss.driver.api.core.NoNodeAvailableException.copy(NoNodeAvailableException.java:40)
at
com.datastax.oss.driver.internal.core.util.concurrent.CompletableFutures.getUninterruptibly(CompletableFutures.java:149)
at
com.datastax.oss.driver.internal.core.cql.MultiPageResultSet$RowIterator.maybeMoveToNextPage(MultiPageResultSet.java:99)
at
com.datastax.oss.driver.internal.core.cql.MultiPageResultSet$RowIterator.computeNext(MultiPageResultSet.java:91)
at
com.datastax.oss.driver.internal.core.cql.MultiPageResultSet$RowIterator.computeNext(MultiPageResultSet.java:79)
at
com.datastax.oss.driver.internal.core.util.CountingIterator.tryToComputeNext(CountingIterator.java:91)
at
com.datastax.oss.driver.internal.core.util.CountingIterator.hasNext(CountingIterator.java:86)
at
com.ngc.helios.fieldanalyzer.FTAProcess.handleOrderedFieldCounts(FTAProcess.java:684)
at
com.ngc.helios.fieldanalyzer.FTAProcess.storeResults(FTAProcess.java:214)
at
com.ngc.helios.fieldanalyzer.FTAProcess.startProcess(FTAProcess.java:190)
at com.ngc.helios.fieldanalyzer.Main.main(Main.java:20)
The interesting part here is the the line that is now failing (line 684 in
FTAProcess) is:
if (itRs.hasNext())
where itRs is an iterator<Row> over a select query from another table. I'm
iterating over a result set from a select and inserting those results via
executeAsync.
-Joe
On 3/12/2021 9:07 AM, Bowen Song wrote:
Millions rows in a single query? That sounds like a bad idea to me. Your
"NoNodeAvailableException" could be caused by stop-the-world GC pauses, and the
GC pauses are likely caused by the query itself.
On 12/03/2021 13:39, Joe Obernberger wrote:
Thank you Paul and Erick. The keyspace is defined like this:
CREATE KEYSPACE doc WITH replication = {'class': 'SimpleStrategy',
'replication_factor': '3'} AND durable_writes = true;
Would that cause this?
The program that is having the problem selects data, calculates stuff, and
inserts. It works with smaller selects, but when the number of rows is in the
millions, I start to get this error. Since it works with smaller sets, I don't
believe it to be a network error. All the nodes are definitely up as other
processes are working OK, it's just this one program that fails.
The full stack trace:
Error: com.datastax.oss.driver.api.core.NoNodeAvailableException: No node was
available to execute the query
com.datastax.oss.driver.api.core.NoNodeAvailableException: No node was
available to execute the query
at
com.datastax.oss.driver.api.core.NoNodeAvailableException.copy(NoNodeAvailableException.java:40)
at
com.datastax.oss.driver.internal.core.util.concurrent.CompletableFutures.getUninterruptibly(CompletableFutures.java:149)
at
com.datastax.oss.driver.internal.core.cql.CqlRequestSyncProcessor.process(CqlRequestSyncProcessor.java:53)
at
com.datastax.oss.driver.internal.core.cql.CqlRequestSyncProcessor.process(CqlRequestSyncProcessor.java:30)
at
com.datastax.oss.driver.internal.core.session.DefaultSession.execute(DefaultSession.java:230)
at
com.datastax.oss.driver.api.core.cql.SyncCqlSession.execute(SyncCqlSession.java:54)
at
com.abc.xxxx.fieldanalyzer.FTAProcess.udpateCassandraFTAMetrics(FTAProcess.java:275)
at
com.abc.xxxx.fieldanalyzer.FTAProcess.storeResults(FTAProcess.java:216)
at
com.abc.xxxx.fieldanalyzer.FTAProcess.startProcess(FTAProcess.java:199)
at com.abc.xxxx.fieldanalyzer.Main.main(Main.java:20)
FTAProcess like 275 is:
ResultSet rs = session.execute(getFieldCounts.bind().setString(0,
rb.getSource()).setString(1, rb.getFieldName()));
-Joe
On 3/12/2021 8:30 AM, Paul Chandler wrote:
Hi Joe
This could also be caused by the replication factor of the keyspace, if you
have NetworkTopologyStrategy and it doesn’t list a replication factor for the
datacenter datacenter1 then you will get this error message too.
Paul
On 12 Mar 2021, at 13:07, Erick Ramirez
<erick.rami...@datastax.com<mailto:erick.rami...@datastax.com>> wrote:
Does it get returned by the driver every single time? The
NoNodeAvailableException gets thrown when (1) all nodes are down, or (2) all
the contact points are invalid from the driver's perspective.
Is it possible there's no route/connectivity from your app server(s) to the
172.16.x.x network? If you post the full error message + full stacktrace, it
might provide clues. Cheers!
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