I saw an average 10% cpu usage on each node when the cassandra cluster has no
load at all.
I checked which thread was using the cpu, and I got the following 2 metric
threads each occupying 5% cpu.
jstack output:
"metrics-meter-tick-thread-2" daemon prio=10 tic=...
java.lang.Thread.State
The cassandra version is 2.0.12. We have 1500 tables in the cluster of 6
nodes, with a total 2.5 billion rows.
在2015年10月24 20时52分, "Xu Zhongxing"写道:
I saw an average 10% cpu usage on each node when the cassandra cluster has no
load at all.
I checked which thread was using the
er the JMX
interfaces.
On Oct 24, 2015, at 7:54 AM, Xu Zhongxing wrote:
The cassandra version is 2.0.12. We have 1500 tables in the cluster of 6
nodes, with a total 2.5 billion rows.
在2015年10月24 20时52分, "Xu Zhongxing"写道:
I saw an average 10% cpu usage on each node when the cassandra clu
Can I run
nodetool upgradesstables
after updating a Cassandra 2.0 node directly to Cassandra 3.0?
Or do I have to upgrade to 2.1 and then upgrade to 3.0?
Hi,
When I connect to C* with driver, I found some warnings in the log (I increased
tombstone_failure_threshold to 15 to see the warning)
WARN [ReadStage:5] 2015-01-13 12:21:14,595 SliceQueryFilter.java (line 225)
Read 34188 live and 104186 tombstoned cells in system.schema_columns (see
I am not sure about the tombstone_failure_threshold, but the tombstones will
only get removed during compaction if they are older than GC_Grace_Seconds for
that CF. How old are these tombstones?
Rahul
On Jan 12, 2015, at 11:27 PM, Xu Zhongxing wrote:
Hi,
When I connect to C* with driver,
Maybe this is the closest thing to "dynamic columns" in CQL 3.
create table reivew (
product_id bigint,
created_at timestamp,
data_key text,
data_tvalue text,
data_ivalue int,
primary key ((priduct_id, created_at), data_key)
);
data_tvalue and data_ivalue is optional.
umns.
On Tue, Jan 20, 2015 at 8:12 PM, Xu Zhongxing wrote:
Maybe this is the closest thing to "dynamic columns" in CQL 3.
create table reivew (
product_id bigint,
created_at timestamp,
data_key text,
data_tvalue text,
data_ivalue int,
primary key ((priduct_
requirement.
he actually needs dynamic columns.
On Tue, Jan 20, 2015 at 8:12 PM, Xu Zhongxing wrote:
Maybe this is the closest thing to "dynamic columns" in CQL 3.
create table reivew (
product_id bigint,
created_at timestamp,
data_key text,
data_tvalue text,
Both Java driver "select * from table" and Spark sc.cassandraTable() work well.
I use both of them frequently.
At 2015-01-28 04:06:20, "Mohammed Guller" wrote:
Hi –
Over the last few weeks, I have seen several emails on this mailing list from
people trying to extract all data from C*, so
The table has several billion rows.
I think the table size is irrelevant here. Cassandra driver can do paging well.
Spark handles data partition well, too.
At 2015-01-28 10:45:17, "Mohammed Guller" wrote:
How big is your table? How much data does it have?
Mohammed
From: Xu
about Java driver. Could you suggest any API you used? Thanks.
On Tue, Jan 27, 2015 at 5:33 PM, Xu Zhongxing wrote:
Both Java driver "select * from table" and Spark sc.cassandraTable() work well.
I use both of them frequently.
At 2015-01-28 04:06:20, "Mohammed Guller" wr
This is hard to answer. The performance is a thing depending on context.
You could tune various parameters.
At 2015-01-28 14:43:38, "Shenghua(Daniel) Wan" wrote:
Cool. What about performance? e.g. how many record for how long?
On Tue, Jan 27, 2015 at 10:16 PM, Xu Zhongxing wrote:
I am using Cassandra's CQLSSTableWriter to import a large amount of data into
Cassandra. When I use CQLSSTableWriter to write to a table with compound
primary key, the memory consumption keeps growing. The GC of JVM cannot collect
any used memory. When writing to tables with no compound primary
Is writing too many rows to a single partition the cause of memory consumption?
What I want to achieve is this: say I have 5 partition ID. Each corresponds to
50 million IDs. Given a partition ID, I need to get its corresponding 50
million IDs. Is there another way to design the schema to avoi
We figured out the reason for the growing memory usage. When adding rows, if
flush-to-disk operation is done in SStableSimpleUnsortedWriter.newRow(). But
for the compound primary key case, when the clustering key is identical, there
is no new row created. So the single huge row is kept in the me
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