jm + hbase   9

How-to: Index Scanned PDFs at Scale Using Fewer Than 50 Lines of Code
using Spark, Tesseract, HBase, Solr and Leptonica. Actually pretty feasible
spark  tesseract  hbase  solr  leptonica  pdfs  scanning  cloudera  hadoop  architecture 
october 2015 by jm
DataSift Architecture: Realtime Datamining at 120,000 Tweets Per Second
250 million tweets per day, 30-node HBase cluster, 400TB of storage, Kafka and 0mq.

This is from 2011, hence this dated line: 'for a distributed application they thought AWS was too limited, especially in the network. AWS doesn’t do well when nodes are connected together and they need to talk to each other. Not low enough latency network. Their customers care about latency.' (Nowadays, it would be damn hard to build a lower-latency network than that attached to a cc2.8xlarge instance.)
datasift  architecture  scalability  data  twitter  firehose  hbase  kafka  zeromq 
april 2013 by jm
Storm and Hadoop: Convergence of Big-Data and Low-Latency Processing
Yahoo! are going big with Storm for their next-generation internal cloud platform:

'Yahoo! engineering teams are developing technologies to enable Storm applications and Hadoop applications to be hosted on a single cluster.

• We have enhanced Storm to support Hadoop style security mechanism (including Kerberos authentication), and thus enable Storm applications authorized to access Hadoop datasets on HDFS and HBase.
• Storm is being integrated into Hadoop YARN for resource management. Storm-on-YARN enables Storm applications to utilize the computation resources in our tens of thousands of Hadoop computation nodes. YARN is used to launch Storm application master (Nimbus) on demand, and enables Nimbus to request resources for Storm application slaves (Supervisors).'
yahoo  yarn  cloud-computing  private-clouds  big-data  latency  storm  hadoop  elastic-computing  hbase 
february 2013 by jm
Cassandra, Hive, and Hadoop: How We Picked Our Analytics Stack
reasonably good whole-stack performance testing and analysis; HBase, Riak, MongoDB, and Cassandra compared. Riak did pretty badly :(
riak  mongodb  cassandra  hbase  performance  analytics  hadoop  hive  big-data  storage  databases  nosql 
february 2013 by jm
HBase Real-time Analytics & Rollbacks via Append-based Updates
Interesting concept for scaling up the write rate on massive key-value counter stores:
'Replace update (Get+Put) operations at write time with simple append-only writes and defer processing of updates to periodic jobs or perform aggregations on the fly if user asks for data earlier than individual additions are processed. The idea is simple and not necessarily novel, but given the specific qualities of HBase, namely fast range scans and high write throughput, this approach works very well.'
counters  analytics  hbase  append  sematext  aggregation  big-data 
december 2012 by jm
Storage Infrastructure Behind Facebook Messages
HBase and Haystack; all data LZO-compressed; very interesting approach to testing -- they 'shadow the real production workload into the test cluster to test before going into production'. This catches a 'high percentage' of issues before production. nice
testing  shadowing  haystack  hbase  facebook  scalability  lzo  messaging  sms  via:james-hamilton 
october 2011 by jm
Avoiding Full GCs in HBase with MemStore-Local Allocation Buffers
Fascinating. Evading the Java GC by reimplementing a slab allocator, basically
memory  allocation  java  gc  jvm  hbase  memstore  via:dehora  slab-allocator 
october 2011 by jm
Facebook's New Realtime Analytics System: HBase to Process 20 Billion Events Per Day
Scribe logs events, "ptail" (parallel tail presumably) tails logs from Scribe stores, Puma batch-aggregates, writes to HBase.  Java and Thrift on the backend, PHP in front
facebook  hbase  scalability  performance  hadoop  scribe  events  analytics  architecture  tail  append  from delicious
march 2011 by jm

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