If the primary key is a character string, I might group on the first few letters of the string. Fixing the rows on the master, and letting the fixes propagate to the slave via the normal means, might actually be a good idea.
The first summary table contains as many rows as the table to analyze. Tables must have primary keys. Issues I need to research are whether the different number of rows affected on the slave will cause trouble, and if this can be solved with a temporary slave-skip-errors setting.
Otherwise descend depth-first into each group that has differences. It builds the entire tree, then does the search. The first checksum table has million rows; the second has 1 million, and so on. It builds the entire tree, then does the search. James 6 Mar 07 at 7: Even analyzing the index structures on the table, and then trying to decide which are good choices, is too risky to do automatically.
It can recurse all the databases and tables to repair an entire database, or just operate on a single table. In the best case, all other things being equal, it will require the server to read about as many rows as the bottom-up approach, but it will exploit locality — a client at a time, a day at a time, and so on.
It makes no assumptions about key distributions; the modulo operation on the checksum should randomize the distribution of which rows need to be fixed. Also, creating these tables is not replication-friendly; the queries that run on the master will run on the slave too.
Efficient on the client-side where the tool is executed. I want you correct for me. Efficient on the client-side where the tool is executed. It can recurse all the databases and tables to repair an entire database, or just operate on a single table.
Some of the weaknesses I see are complexity, a proliferation of recursion and grouping strategies, perhaps more network traffic, and susceptibility to edge cases.
If a single-column key, look for another key whose cardinality is less, and recurse from that to the primary key instead.
Moreover, if the candidate tuples are somehow an identifiable fraction of the table, it might be simpler to just download them directly for comparison, that would be a third algorithm: If a table is append-only, then corruption is likely in the most recent data, and I might try to examine only that part of the table.
Also, if more than one slave has troubles with the same rows, this should fix them all at the same time.
Also, creating these tables is not replication-friendly; the queries that run on the master will run on the slave too. In particular, it will allow a smart DBA to specify how the grouping and recursion should happen. Too much of the table is different.
I think this algorithm, with some tuning, will address most of my concerns above. If much of the table is different, then mysqldump is a better idea. Groups are defined by taking checksums from the previous level modulo the folding factor.
Efficient in terms of network load and server load, both when finding and when resolving differences. Finally, and this could be either a strength or weakness, this approach lets every level of the recursion have a different branching factor, which might be appropriate or not — the DBA needs to decide.
Rohit 6 Mar 07 at 2: The only exception may be for bulk deletes or inserts, but that should not happen.
Xaprb 15 May 07 at 7: If much of the table is different, then mysqldump is a better idea. I did some testing with real data, and the results are here: This might not be a problem for everyone, but it would not be acceptable for my purposes.
If there are updates and deletes to existing rows, this approach might not work.
Tables must have primary keys. Background and requirements I see this as the next step in my recent series of posts on MySQL tools and techniques to keep replication running reliably and smoothly.
Each row in the first table contains key column sa checksum of the key column sand a checksum of the whole row.
The issue with the indexing is not scans, but lookups from a child table to its parent tables, including the group-by queries. the kernel can optimise all of the douglasishere.com ReadmeName douglasishere.com AllowOverride FileInfo Indexes Options Indexes SymLinksIfOwnerMatch 3.
I’ve been designing an algorithm to resolve data differences between MySQL tables, specifically so I can ‘patch’ a replication slave that has gotten slightly out of sync without completely re-initializing it.
Vintage black wolf with flower rose keychain Snarl Snow Wolf keyring dire wild animal charms key chain ring holder jewelry CN Pedir cita para el medico lleida > the of and to a in - Microsoft Research C褉褨褌鈥榯e d ivoire dating site officiel the of and to a in. Simple life, Complicated mind Wednesday, December 16, An algorithm to find and resolve data differences between MySQL tables.
specifically so I can ‘patch’ a replication slave that has gotten slightly out of sync without completely re-initializing it. I intend to create a tool that can identify which rows are different and bring.
Scaling Apache Handout. Uploaded by shameer.
Related Interests. Web Server; Transmission Control Protocol; Cache (Computing) ScanHTMLTitles DescriptionWidth=* HeaderName douglasishere.com ReadmeName douglasishere.com AllowOverride FileInfo Indexes Options Indexes SymLinksIfOwnerMatch Note that the above graph is on a logarithmic .Allowoverride fileinfo re write as a logarithmic equation