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PostgreSQL 9.0 : high performance

Author: Gregory Smith
Publisher: Birmingham [England] : Packt Pub., 2010.
Series: Open source : community experience distilled.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
In Detail PostgreSQL database servers have a common set of problems they encounter as their usage gets heavier and requirements more demanding. You could spend years discovering solutions to them all, step by step as you encounter them. Or you can just look in here. All successful database applications are destined to eventually run into issues scaling up their performance. Peek into the future of your PostgreSQL  Read more...
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Gregory Smith
ISBN: 9781849510318 1849510318 184951030X 9781849510301
OCLC Number: 694147032
Description: 1 online resource (xiii, 442 pages) : illustrations.
Contents: PostgreSQL 9.0 High Performance --
PostgreSQL 9.0 High Performance --
Credits --
About the Author --
About the Reviewers --
Preface --
What this book covers --
What you need for this book --
Who this book is for --
Conventions --
Reader feedback --
Customer support --
Errata --
Piracy --
Questions --
1. PostgreSQL Versions --
Performance of historical PostgreSQL releases --
Choosing a version to deploy --
Upgrading to a newer major version --
Upgrades to PostgreSQL 8.3+ from earlier ones --
Minor version upgrades --
PostgreSQL or another database? --
PostgreSQL tools --
PostgreSQL contrib --
Finding contrib modules on your system --
Installing a contrib module from source --
Using a contrib module --
pgFoundry --
Additional PostgreSQL-related software --
PostgreSQL application scaling lifecycle --
Performance tuning as a practice --
Summary --
2. Database Hardware --
Balancing hardware spending --
CPUs --
Memory --
Disks --
RAID --
Drive error handling --
Hard drive reliability studies --
Drive firmware and RAID --
SSDs --
Disk controllers --
Hardware and Software RAID --
Recommended disk controllers --
Attached storage-SAN and NAS --
Reliable controller and disk setup --
Write-back caches --
Sources of write-back caching --
Disk controller monitoring --
Disabling drive write caches --
Performance impact of write-through caching --
Summary --
3. Database Hardware Benchmarking --
CPU and memory benchmarking --
memtest86+ --
STREAM memory testing --
STREAM and Intel vs. AMD --
CPU benchmarking --
Sources of slow memory and processors --
Physical disk performance --
Random access and I/Os Per Second --
Sequential access and ZCAV --
Short stroking --
Commit rate --
PostgreSQL test_fsync --
INSERT rate --
Windows commit rate --
Disk benchmarking tools --
hdtune --
Short stroking tests --
IOPS --
Unpredictable performance and Windows --
dd --
bonnie++. Bonnie++ 2.0 --
bonnie++ ZCAV --
sysbench --
Seek rate --
fsync commit rate --
Complicated disk benchmarks --
Sample disk results --
Disk performance expectations --
Sources of slow disk and array performance --
Summary --
4. Disk Setup --
Maximum filesystem sizes --
Filesystem crash recovery --
Journaling filesystems --
Linux filesystems --
ext2 --
ext3 --
ext4 --
XFS --
Other Linux filesystems --
Write barriers --
Drive support for barriers --
Filesystem support for barriers --
General Linux filesystem tuning --
Read-ahead --
File access times --
Read caching and swapping --
Write cache sizing --
I/O scheduler elevator --
Solaris and FreeBSD filesystems --
Solaris UFS --
FreeBSD UFS2 --
ZFS --
Windows filesystems --
FAT32 --
NTFS --
Adjusting mounting behaviour --
Disk layout for PostgreSQL --
Symbolic links --
Tablespaces --
Database directory tree --
Temporary files --
Disk arrays, RAID, and disk layout --
Disk layout guidelines --
Summary --
5. Memory for Database Caching --
Memory units in the postgresql.conf --
Increasing UNIX shared memory parameters for larger buffer sizes --
Kernel semaphores --
Estimating shared memory allocation --
Inspecting the database cache --
Installing pg_buffercache into a database --
Database disk layout --
Creating a new block in a database --
Writing dirty blocks to disk --
Crash recovery and the buffer cache --
Checkpoint processing basics --
Write-ahead log and recovery processing --
Checkpoint timing --
Checkpoint spikes --
Spread checkpoints --
Database block lifecycle --
Dirty block write paths --
Database buffer cache versus operating system cache --
Doubly cached data --
Inspecting the OS cache --
Checkpoint overhead --
Starting size guidelines --
Platform, version, and workload limitations --
Analyzing buffer cache contents --
Inspection of the buffer cache queries --
Top relations in the cache. Autovacuum is constantly running --
Out of memory errors --
Not keeping up on a busy server --
autovacuum is too disruptive --
Long running transactions --
Free Space Map exhaustion --
Recovering from major problems --
Autoanalyze --
Index bloat --
Measuring index bloat --
Detailed data and index page monitoring --
Monitoring query logs --
Basic PostgreSQL log setup --
Log collection --
log_line_prefix --
Multi-line queries --
Using syslog for log messages --
CSV logging --
Logging difficult queries --
auto_explain --
Log file analysis --
Normalized query fingerprints --
pg_stat_statements --
pgFouine --
PQA --
EPQA --
pgsi --
mk-query-digest --
Summary --
8. Database Benchmarking --
pgbench default tests --
Table definition --
Scale detection --
Query script definition --
Configuring the database server for pgbench --
Sample server configuration --
Running pgbench manually --
Graphing results with pgbench-tools --
Configuring pgbench-tools --
Customizing for 8.3 --
Sample pgbench test results --
SELECT-only test --
TPC-B-like test --
Latency analysis --
Sources for bad results and variation --
Developer PostgreSQL builds --
Worker threads and pgbench program limitations --
pgbench custom tests --
Insert speed test --
Transaction Processing Performance Council benchmarks --
Summary --
9. Database Indexing --
Indexing example walkthrough --
Measuring query disk and index block statistics --
Running the example --
Sample data setup --
Simple index lookups --
Full table scans --
Index creation --
Lookup with an inefficient index --
Combining indexes --
Switching from indexed to sequential scans --
Planning for plan changes --
Clustering against an index --
Explain with buffer counts --
Index creation and maintenance --
Unique indexes --
Concurrent index creation --
Clustering an index --
Fill factor --
Reindexing --
Index types --
B-tree. Text operator classes --
Hash --
GIN --
GiST --
Advanced index use --
Multicolumn indexes --
Indexes for sorting --
Partial indexes --
Expression-based indexes --
Indexing for full-text search --
Summary --
10. Query Optimization --
Sample data sets --
Pagila --
Dell Store 2 --
EXPLAIN basics --
Timing overhead --
Hot and cold cache behavior --
Clearing the cache --
Query plan node structure --
Basic cost computation --
Estimated costs and real world costs --
Explain analysis tools --
Visual explain --
Verbose output --
Machine readable explain output --
Plan analysis tools --
Assembling row sets --
Tuple id --
Object id --
Sequential scan --
Index scan --
Bitmap heap and index scans --
Processing nodes --
Sort --
Limit --
Offsets --
Aggregate --
HashAggregate --
Unique --
WindowAgg --
Result --
Append --
Group --
Subquery Scan and Subplan --
Subquery conversion and IN lists --
Set operations --
Materialize --
CTE Scan --
Joins --
Nested loop --
Nested loop with inner Index Scan --
Merge Join --
Nested loop and Merge Join materialization --
Hash Joins --
Hash semi and anti joins --
Join ordering --
Forcing join order --
Join removal --
Genetic query optimizer --
Statistics --
Viewing and estimating with statistics --
Statistics targets --
Adjusting a column target --
Distinct values --
Difficult areas to estimate --
Other query planning parameters --
effective_cache_size --
work_mem --
constraint_exclusion --
cursor_tuple_fraction --
Executing other statement types --
Improving queries --
Optimizing for fully cached data sets --
Testing for query equivalence --
Disabling optimizer features --
Working around optimizer bugs --
Avoiding plan restructuring with OFFSET --
External trouble spots --
SQL Limitations --
Numbering rows in SQL --
Using Window functions for numbering --
Using Window functions for cumulatives --
Summary.
Series Title: Open source : community experience distilled.
Responsibility: Gregory Smith.

Abstract:

In Detail PostgreSQL database servers have a common set of problems they encounter as their usage gets heavier and requirements more demanding. You could spend years discovering solutions to them all, step by step as you encounter them. Or you can just look in here. All successful database applications are destined to eventually run into issues scaling up their performance. Peek into the future of your PostgreSQL database's problems today. Know the warning signs to look for, and how to avoid the most common issues before they even happen. Surprisingly, most PostgreSQL database applications evolve in the same way: Choose the right hardware. Tune the operating system and server memory use. Optimize queries against the database, with the right indexes. Monitor every layer, from hardware to queries, using some tools that are inside PostgreSQL and others that are external. Using monitoring insight, continuously rework the design and configuration. On reaching the limits of a single server, break things up; connection pooling, caching, partitioning, and replication can all help handle increasing database workloads. The path to a high performance database system isn't always easy. But it doesn't have to be mysterious with the right guide. A clear, step-by-step guide to optimizing and scaling up PostgreSQL database serversApproach Improving database performance requires an equal mix of understanding theoretical concepts and working through hands-on examples. You'll find both here. Many of the examples given will be immediately useful for monitoring and improving your PostgreSQL deployments, providing insight into hard-to-obtain information about your database. Who this book is for This book is aimed at intermediate to advanced database administrators using or planning to use PostgreSQL. Portions will also interest systems administrators looking to build or monitor a PostgreSQL installation, as well as developers interested in advanced database internals that impact application design.

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