cassandra-2.1

Time series modelling( with start & end date) in cassandra

由 痴心易碎 提交于 2019-12-04 04:38:16
问题 I am doing time series data modelling where I have a start date and end date of events. I need to query on that data model like the following: Select * from tablename where startdate>'2012-08-09' and enddate<'2012-09-09' I referred to the following link on cql where clause but I couldn't achieve this. Any way to do that? I can also change the data model or any cql tweaks. I am using Cassandra 2.1. 回答1: I had to solve a similar problem in one of my former positions. This is one way in which

How to prevent Cassandra commit logs filling up disk space

由 谁说我不能喝 提交于 2019-12-04 01:15:23
问题 I'm running a two node Datastax AMI cluster on AWS. Yesterday, Cassandra started refusing connections from everything. The system logs showed nothing. After a lot of tinkering, I discovered that the commit logs had filled up all the disk space on the allotted mount and this seemed to be causing the connection refusal (deleted some of the commit logs, restarted and was able to connect). I'm on DataStax AMI 2.5.1 and Cassandra 2.1.7 If I decide to wipe and restart everything from scratch, how

How does the Leveled Compaction Strategy ensure 90% of reads are from one sstable

由 空扰寡人 提交于 2019-12-03 13:13:53
问题 I am trying to understand how the Leveled Compaction Strategy in Cassandra works that guarantees 90% of all reads will be satisfied from a single sstable. From DataStax Doc: new sstables are added to the first level, L0, and immediately compacted with the sstables in L1. When L1 fills up, extra sstables are promoted to L2. Subsequent sstables generated in L1 will be compacted with the sstables in L2 with which they overlap. 回答1: LeveledCompactionStrategy (LCS) in Cassandra implements the

Max. size of wide rows?

由 故事扮演 提交于 2019-12-03 09:00:44
问题 Theoretically, Cassandra allows up to 2 billion columns in a wide row. I have heard that in reality up to 50.000 cols/50 MB are fine; 50.000-100.000 cols/100 MB are OK but require some tuning; and that one should never go above 100.000/100 MB columns per row. The reason being that this will put pressure on the heap. Is there some truth to this? 回答1: In Cassandra, the maximum number of cells (rows x columns) in a single partition is 2 billion. Additionally, a single column value may not be

How does the Leveled Compaction Strategy ensure 90% of reads are from one sstable

由 谁都会走 提交于 2019-12-03 03:20:24
I am trying to understand how the Leveled Compaction Strategy in Cassandra works that guarantees 90% of all reads will be satisfied from a single sstable. From DataStax Doc: new sstables are added to the first level, L0, and immediately compacted with the sstables in L1. When L1 fills up, extra sstables are promoted to L2. Subsequent sstables generated in L1 will be compacted with the sstables in L2 with which they overlap. LeveledCompactionStrategy (LCS) in Cassandra implements the internals of LevelDB. You can check the exact implementation details in LevelDB implementation doc . In order to

Max. size of wide rows?

由 无人久伴 提交于 2019-12-02 23:11:17
Theoretically, Cassandra allows up to 2 billion columns in a wide row. I have heard that in reality up to 50.000 cols/50 MB are fine; 50.000-100.000 cols/100 MB are OK but require some tuning; and that one should never go above 100.000/100 MB columns per row. The reason being that this will put pressure on the heap. Is there some truth to this? In Cassandra, the maximum number of cells (rows x columns) in a single partition is 2 billion . Additionally, a single column value may not be larger than 2GB, but in practice, "single digits of MB" is a more reasonable limit, since there is no

Best practices on upgrading cassandra

由 折月煮酒 提交于 2019-12-02 16:33:15
问题 I am getting errors from pyspark connecting to cassandra because it appears I am using a too old a cassandra: [idf@node1 python]$ nodetool -h localhost version ReleaseVersion: 2.0.17 [idf@node1 python]$ [idf@node1 cassandra]$ java --version Unrecognized option: --version Error: Could not create the Java Virtual Machine. Error: A fatal exception has occurred. Program will exit. [idf@node1 cassandra]$ java -version java version "1.8.0_45" Java(TM) SE Runtime Environment (build 1.8.0_45-b14)

Best practices on upgrading cassandra

由 柔情痞子 提交于 2019-12-02 10:11:04
I am getting errors from pyspark connecting to cassandra because it appears I am using a too old a cassandra: [idf@node1 python]$ nodetool -h localhost version ReleaseVersion: 2.0.17 [idf@node1 python]$ [idf@node1 cassandra]$ java --version Unrecognized option: --version Error: Could not create the Java Virtual Machine. Error: A fatal exception has occurred. Program will exit. [idf@node1 cassandra]$ java -version java version "1.8.0_45" Java(TM) SE Runtime Environment (build 1.8.0_45-b14) Java HotSpot(TM) 64-Bit Server VM (build 25.45-b02, mixed mode) [idf@node1 cassandra]$ I want to upgrade

Cassandra upgrade from 2.0.x to 2.1.x or 3.0.x

由 风流意气都作罢 提交于 2019-12-01 12:18:15
I've searched for previous versions of this question, but none seem to fit my case. I have an existing Cassandra cluster running 2.0.x. I've been allocated new VMs, so I do NOT want to upgrade my existing Cassandra nodes - rather I want to migrate to a) new VMs and b) a more current version of Cassandra. I know for in-place upgrades, I would upgrade to the latest 2.0.x, then to the latest 2.1.x. AFAIK, there's no SSTable inconsistency here. If I go this route via addition of new nodes, I assume I would follow the datastax instructions for adding new nodes/decommissioning old nodes? Given the

Cassandra upgrade from 2.0.x to 2.1.x or 3.0.x

由 邮差的信 提交于 2019-12-01 11:02:58
问题 I've searched for previous versions of this question, but none seem to fit my case. I have an existing Cassandra cluster running 2.0.x. I've been allocated new VMs, so I do NOT want to upgrade my existing Cassandra nodes - rather I want to migrate to a) new VMs and b) a more current version of Cassandra. I know for in-place upgrades, I would upgrade to the latest 2.0.x, then to the latest 2.1.x. AFAIK, there's no SSTable inconsistency here. If I go this route via addition of new nodes, I
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