They all have their open source implementation. Not Supported. BigTable only supports transactions on a single row. It does not support transactions spanning multiple rows. Column Family. BigTable does not support relational data model. Instead, it provides users the ability to create column families in a table.
Each table usually contains a small number of column families, which should be rarely changed because the change of them involves metadata change. Inside each column family, there can be unlimited number of columns.
Users can freely add or delete columns in a column family. Deleting of an entire column family is also supported.
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BigTable does not have any type information associated with a given column. It only treats data as strings of bytes. Physical Logging.
BigTable uses physical logging. For performance consideration, all tablets on a tablet server write logs to the same log file. Custom API. BigTable provides clients with the following APIs: 1.
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Look Up Read a Single Row 2. Scan Read a subset of rows 3. Write 4. Delete 5. Customized Scripts written in Sawzall language. BigTable assumes an underlying reliable distributed file system here is Google File System. The tablets are stored in Google File System, which is a disk-oriented file system.
The most recently written records are stored in memtable, which is in memory. However, most of the data is stored on disk. In BigTable, a table is split into multiple tablets, each of which is a subset of consecutive rows. A tablet is a unit of data distribution and load balancing.
Such an approach would not have been acceptable for OLTP scenario, but is the best choice for Google use case. The latency is not a problem like in an OLTP scenario. Tests have been made by the teams and increasing the number of concurrent threads allows them to keep a good throughput. I have learned from this transactional architecture that a good understanding of the needs, of the constraints that can be relaxed and a clear understanding of the consequences of choices are the best means to identify your architecture.
First (Paper) Love
XA transactions distributed transactions have been known for a long time. I have already been confronted to concurrency issues with XA transactions without finding a satisfactory issue. What I have learned today is that distributed transactions are complex but can be useful if adapted to the business need. Percolator has been designed as an incremental system. Observers are designed for that goal: Percolator is designed as a series of observers. For an observed column, an entry is written in the c:notify column each time data is written in the c:data column.
Observers are implemented in a separate worker process.
bigtable: how does bigtable serves write request? - Stack Overflow
This worker process performs a distributed scan to find the row that has an entry in the c:notify column. This column is not a transactional one and many writes to the column may trigger the observer only once. Please refer to the whole paper for further information. Percolator system has been built on top of an existing layer and in a distributed way. The provided scalability comes at a cost.
Last, releasing locks in a lazy way combined with blocking API causes contention. These two issues have been resolved:.
To summarize, Percolator reached its goal as it allows reducing the latency time between page crawling and availability in the index by a factor of It allows simplifying the algorithm. The big advantage of Percolator is that the indexing time is now proportional to the size of the page to index and no more to the whole existing index size. In my past experience I have seen difficulties when some batches, for loading start of day data, were replaced by EAI message driven middleware without changing the timeline.
Such results tend to confirm my point of view: batch and on the fly processing require different kind of architecture. Some measures were then performed. Last benchmark is based on TPC-E to be able to compare percolator to databases. Some adaptations have been made: one update has been deactivated due to conflict — another implementation was described but has not been measured — and the latencies are larger than the maximum bearable for an OLTP workload.
Despite all those limitations the important results were:. The gain in scalability and resilience comes at a cost. In particular layering causes the most important overhead. Google concluded that TPC-E results suggest a promising direction for future investigation. Scalability has strongly modified application architecture, in particular with the web.
Distribution of the data has allowed Google and other big internet actors to reach scalability unbelievable till now. However, as a result of the distribution, there is a performance penalty. Such NoSQL architectures are inspiring influence for use cases or future evolution in application design e. Faster CPUs than disks have made multi-CPU shared memory system aka monolithic systems simpler competitive to the distribution approach for databases.
Today, huge datasets like Google index processed by Percolator changes this situation and a distributed architecture is required. Percolator has been in production since April and achieves the goal.
It's all in the terminology
TPC-E benchmark is promising but the linear scalability comes at a fold overhead compared to a traditional database. The next challenge, at the end of this research paper, would be to know if it can be tuned or if it is inherent to distributed systems. Such in-depth panorama of Percolator architecture was for me a good way to clearly understand the way the architecture was built, from constraint analysis to optimizations. Even if it is not directly applicable to day to day work, Google is one of the key innovative leaders for large scale architecture.
Analyzing such architecture enabled me to stand back from traditional architectures. Your email address will not be published. Me notifier par mail en cas de nouveaux commentaires.