Ramirez,
If you partition your data correctly speed will be ~proportional. But there's always an upper limit - a slow range query that executes on a single node (using cluster key) will always be a slow. Cheers, Jens — Sent from Mailbox On Sun, Jul 6, 2014 at 8:04 AM, Rameez Thonnakkal <ssram...@gmail.com> wrote: > Won't the performeance improve significantly if you increase the number of > nodes even in a commodity hardware profile. > On 5 Jul 2014 01:38, "Jens Rantil" <jens.ran...@tink.se> wrote: >> Hi Mike, >> >> To learn get subsecond performance on your queries using _any_ database >> you need to use proper indexing. Like Jeremy said, Solr will do this. >> >> If you'd like to try to solve this using Cassandra you need to learn the >> difference between partition and clustering in your primary key and >> understand you need a clustering to do any kind of range query. >> >> Also, COUNTs in Cassandra are generally fairly slow. >> >> Cheers, >> Jens >> — >> Sent from Mailbox <https://www.dropbox.com/mailbox> >> >> >> On Tue, Jun 24, 2014 at 10:09 AM, Mike Carter <jaloos...@gmail.com> wrote: >> >>> Hello! >>> >>> >>> I'm a beginner in C* and I'm quite struggling with it. >>> >>> I’d like to measure the performance of some Cassandra-Range-Queries. The >>> idea is to execute multidimensional range-queries on Cassandra. E.g. there >>> is a given table of 1million rows with 10 columns and I like to execute >>> some queries like “select count(*) from testable where d=1 and v1<10 and v2 >>> >20 and v3 <45 and v4>70 … allow filtering”. This kind of queries is very >>> slow in C* and soon the tables are bigger, I get a read-timeout probably >>> caused by long scan operations. >>> >>> In further tests I like to extend the dimensions to more than 200 >>> hundreds and the rows to 100millions, but actually I can’t handle this >>> small table. Should reorganize the data or is it impossible to perform such >>> high multi-dimensional queries on Cassandra? >>> >>> >>> >>> >>> >>> The setup: >>> >>> Cassandra is installed on a single node with 2 TB disk space and 180GB >>> Ram. >>> >>> Connected to Test Cluster at localhost:9160. >>> >>> [cqlsh 4.1.1 | Cassandra 2.0.7 | CQL spec 3.1.1 | Thrift protocol 19.39.0] >>> >>> >>> >>> Keyspace: >>> >>> CREATE KEYSPACE test WITH replication = { >>> >>> 'class': 'SimpleStrategy', >>> >>> 'replication_factor': '1' >>> >>> }; >>> >>> >>> >>> >>> >>> Table: >>> >>> CREATE TABLE testc21 ( >>> >>> key int, >>> >>> d int, >>> >>> v1 int, >>> >>> v10 int, >>> >>> v2 int, >>> >>> v3 int, >>> >>> v4 int, >>> >>> v5 int, >>> >>> v6 int, >>> >>> v7 int, >>> >>> v8 int, >>> >>> v9 int, >>> >>> PRIMARY KEY (key) >>> >>> ) WITH >>> >>> bloom_filter_fp_chance=0.010000 AND >>> >>> caching='ROWS_ONLY' AND >>> >>> comment='' AND >>> >>> dclocal_read_repair_chance=0.000000 AND >>> >>> gc_grace_seconds=864000 AND >>> >>> index_interval=128 AND >>> >>> read_repair_chance=0.100000 AND >>> >>> replicate_on_write='true' AND >>> >>> populate_io_cache_on_flush='false' AND >>> >>> default_time_to_live=0 AND >>> >>> speculative_retry='99.0PERCENTILE' AND >>> >>> memtable_flush_period_in_ms=0 AND >>> >>> compaction={'class': 'SizeTieredCompactionStrategy'} AND >>> >>> compression={'sstable_compression': 'LZ4Compressor'}; >>> >>> >>> >>> CREATE INDEX testc21_d_idx ON testc21 (d); >>> >>> >>> >>> select * from testc21 limit 10; >>> >>> key | d | v1 | v10 | v2 | v3 | v4 | v5 | v6 | v7 | v8 | v9 >>> >>> --------+---+----+-----+----+----+-----+----+----+----+----+----- >>> >>> 302602 | 1 | 56 | 55 | 26 | 45 | 67 | 75 | 25 | 50 | 26 | 54 >>> >>> 531141 | 1 | 90 | 77 | 86 | 42 | 76 | 91 | 47 | 31 | 77 | 27 >>> >>> 693077 | 1 | 67 | 71 | 14 | 59 | 100 | 90 | 11 | 15 | 6 | 19 >>> >>> 4317 | 1 | 70 | 77 | 44 | 77 | 41 | 68 | 33 | 0 | 99 | 14 >>> >>> 927961 | 1 | 15 | 97 | 95 | 80 | 35 | 36 | 45 | 8 | 11 | 100 >>> >>> 313395 | 1 | 68 | 62 | 56 | 85 | 14 | 96 | 43 | 6 | 32 | 7 >>> >>> 368168 | 1 | 3 | 63 | 55 | 32 | 18 | 95 | 67 | 78 | 83 | 52 >>> >>> 671830 | 1 | 14 | 29 | 28 | 17 | 42 | 42 | 4 | 6 | 61 | 93 >>> >>> 62693 | 1 | 26 | 48 | 15 | 22 | 73 | 94 | 86 | 4 | 66 | 63 >>> >>> 488360 | 1 | 8 | 57 | 86 | 31 | 51 | 9 | 40 | 52 | 91 | 45 >>> >>> Mike >>> >> >>
