| F.1. amcheck — tools to verify table and index consistency | ||||
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F.1. amcheck — tools to verify table and index consistency#
Theamcheck module provides functions that allow you to verify the logical consistency of the structure of relations.
The B-Tree checking functions verify variousinvariants in the structure of the representation of particular relations. The correctness of the access method functions behind index scans and other important operations relies on these invariants always holding. For example, certain functions verify, among other things, that all B-Tree pages have items in“logical” order (e.g., for B-Tree indexes ontext, index tuples should be in collated lexical order). If that particular invariant somehow fails to hold, we can expect binary searches on the affected page to incorrectly guide index scans, resulting in wrong answers to SQL queries. If the structure appears to be valid, no error is raised. While these checking functions are run, thesearch_path is temporarily changed topg_catalog, pg_temp.
Verification is performed using the same procedures as those used by index scans themselves, which may be user-defined operator class code. For example, B-Tree index verification relies on comparisons made with one or more B-Tree support function 1 routines. SeeSection 36.16.3 for details of operator class support functions.
Unlike the B-Tree checking functions which report corruption by raising errors, the heap checking functionverify_heapam checks a table and attempts to return a set of rows, one row per corruption detected. Despite this, if facilities thatverify_heapam relies upon are themselves corrupted, the function may be unable to continue and may instead raise an error.
Permission to executeamcheck functions may be granted to non-superusers, but before granting such permissions careful consideration should be given to data security and privacy concerns. Although the corruption reports generated by these functions do not focus on the contents of the corrupted data so much as on the structure of that data and the nature of the corruptions found, an attacker who gains permission to execute these functions, particularly if the attacker can also induce corruption, might be able to infer something of the data itself from such messages.
F.1.1. Functions#
bt_index_check(index regclass, heapallindexed boolean, checkunique boolean) returns voidbt_index_checktests that its target, a B-Tree index, respects a variety of invariants. Example usage:test=# SELECT bt_index_check(index => c.oid, heapallindexed => i.indisunique), c.relname, c.relpagesFROM pg_index iJOIN pg_opclass op ON i.indclass[0] = op.oidJOIN pg_am am ON op.opcmethod = am.oidJOIN pg_class c ON i.indexrelid = c.oidJOIN pg_namespace n ON c.relnamespace = n.oidWHERE am.amname = 'btree' AND n.nspname = 'pg_catalog'-- Don't check temp tables, which may be from another session:AND c.relpersistence != 't'-- Function may throw an error when this is omitted:AND c.relkind = 'i' AND i.indisready AND i.indisvalidORDER BY c.relpages DESC LIMIT 10; bt_index_check | relname | relpages----------------+---------------------------------+---------- | pg_depend_reference_index | 43 | pg_depend_depender_index | 40 | pg_proc_proname_args_nsp_index | 31 | pg_description_o_c_o_index | 21 | pg_attribute_relid_attnam_index | 14 | pg_proc_oid_index | 10 | pg_attribute_relid_attnum_index | 9 | pg_amproc_fam_proc_index | 5 | pg_amop_opr_fam_index | 5 | pg_amop_fam_strat_index | 5(10 rows)
This example shows a session that performs verification of the 10 largest catalog indexes in the database“test”. Verification of the presence of heap tuples as index tuples is requested for the subset that are unique indexes. Since no error is raised, all indexes tested appear to be logically consistent. Naturally, this query could easily be changed to call
bt_index_checkfor every index in the database where verification is supported.bt_index_checkacquires anAccessShareLockon the target index and the heap relation it belongs to. This lock mode is the same lock mode acquired on relations by simpleSELECTstatements.bt_index_checkdoes not verify invariants that span child/parent relationships, but will verify the presence of all heap tuples as index tuples within the index whenheapallindexedistrue. Whencheckuniqueistruebt_index_checkwill check that no more than one among duplicate entries in unique index is visible. When a routine, lightweight test for corruption is required in a live production environment, usingbt_index_checkoften provides the best trade-off between thoroughness of verification and limiting the impact on application performance and availability.bt_index_parent_check(index regclass, heapallindexed boolean, rootdescend boolean, checkunique boolean) returns voidbt_index_parent_checktests that its target, a B-Tree index, respects a variety of invariants. Optionally, when theheapallindexedargument istrue, the function verifies the presence of all heap tuples that should be found within the index. Whencheckuniqueistruebt_index_parent_checkwill check that no more than one among duplicate entries in unique index is visible. When the optionalrootdescendargument istrue, verification re-finds tuples on the leaf level by performing a new search from the root page for each tuple. The checks that can be performed bybt_index_parent_checkare a superset of the checks that can be performed bybt_index_check.bt_index_parent_checkcan be thought of as a more thorough variant ofbt_index_check: unlikebt_index_check,bt_index_parent_checkalso checks invariants that span parent/child relationships, including checking that there are no missing downlinks in the index structure.bt_index_parent_checkfollows the general convention of raising an error if it finds a logical inconsistency or other problem.A
ShareLockis required on the target index bybt_index_parent_check(aShareLockis also acquired on the heap relation). These locks prevent concurrent data modification fromINSERT,UPDATE, andDELETEcommands. The locks also prevent the underlying relation from being concurrently processed byVACUUM, as well as all other utility commands. Note that the function holds locks only while running, not for the entire transaction.bt_index_parent_check's additional verification is more likely to detect various pathological cases. These cases may involve an incorrectly implemented B-Tree operator class used by the index that is checked, or, hypothetically, undiscovered bugs in the underlying B-Tree index access method code. Note thatbt_index_parent_checkcannot be used when hot standby mode is enabled (i.e., on read-only physical replicas), unlikebt_index_check.
Tip
bt_index_check andbt_index_parent_check both output log messages about the verification process atDEBUG1 andDEBUG2 severity levels. These messages provide detailed information about the verification process that may be of interest toPostgreSQL developers. Advanced users may also find this information helpful, since it provides additional context should verification actually detect an inconsistency. Running:
SET client_min_messages = DEBUG1;
in an interactivepsql session before running a verification query will display messages about the progress of verification with a manageable level of detail.
verify_heapam(relation regclass, on_error_stop boolean, check_toast boolean, skip text, startblock bigint, endblock bigint, blkno OUT bigint, offnum OUT integer, attnum OUT integer, msg OUT text) returns setof recordChecks a table, sequence, or materialized view for structural corruption, where pages in the relation contain data that is invalidly formatted, and for logical corruption, where pages are structurally valid but inconsistent with the rest of the database cluster.
The following optional arguments are recognized:
on_error_stopIf true, corruption checking stops at the end of the first block in which any corruptions are found.
Defaults to false.
check_toastIf true, toasted values are checked against the target relation's TOAST table.
This option is known to be slow. Also, if the toast table or its index is corrupt, checking it against toast values could conceivably crash the server, although in many cases this would just produce an error.
Defaults to false.
skipIf not
none, corruption checking skips blocks that are marked as all-visible or all-frozen, as specified. Valid options areall-visible,all-frozenandnone.Defaults to
none.startblockIf specified, corruption checking begins at the specified block, skipping all previous blocks. It is an error to specify a
startblockoutside the range of blocks in the target table.By default, checking begins at the first block.
endblockIf specified, corruption checking ends at the specified block, skipping all remaining blocks. It is an error to specify an
endblockoutside the range of blocks in the target table.By default, all blocks are checked.
For each corruption detected,
verify_heapamreturns a row with the following columns:blknoThe number of the block containing the corrupt page.
offnumThe OffsetNumber of the corrupt tuple.
attnumThe attribute number of the corrupt column in the tuple, if the corruption is specific to a column and not the tuple as a whole.
msgA message describing the problem detected.
F.1.2. Optionalheapallindexed Verification#
When theheapallindexed argument to B-Tree verification functions istrue, an additional phase of verification is performed against the table associated with the target index relation. This consists of a“dummy”CREATE INDEX operation, which checks for the presence of all hypothetical new index tuples against a temporary, in-memory summarizing structure (this is built when needed during the basic first phase of verification). The summarizing structure“fingerprints” every tuple found within the target index. The high level principle behindheapallindexed verification is that a new index that is equivalent to the existing, target index must only have entries that can be found in the existing structure.
The additionalheapallindexed phase adds significant overhead: verification will typically take several times longer. However, there is no change to the relation-level locks acquired whenheapallindexed verification is performed.
The summarizing structure is bound in size bymaintenance_work_mem. In order to ensure that there is no more than a 2% probability of failure to detect an inconsistency for each heap tuple that should be represented in the index, approximately 2 bytes of memory are needed per tuple. As less memory is made available per tuple, the probability of missing an inconsistency slowly increases. This approach limits the overhead of verification significantly, while only slightly reducing the probability of detecting a problem, especially for installations where verification is treated as a routine maintenance task. Any single absent or malformed tuple has a new opportunity to be detected with each new verification attempt.
F.1.3. Usingamcheck Effectively#
amcheck can be effective at detecting various types of failure modes thatdata checksums will fail to catch. These include:
Structural inconsistencies caused by incorrect operator class implementations.
This includes issues caused by the comparison rules of operating system collations changing. Comparisons of datums of a collatable type like
textmust be immutable (just as all comparisons used for B-Tree index scans must be immutable), which implies that operating system collation rules must never change. Though rare, updates to operating system collation rules can cause these issues. More commonly, an inconsistency in the collation order between a primary server and a standby server is implicated, possibly because themajor operating system version in use is inconsistent. Such inconsistencies will generally only arise on standby servers, and so can generally only be detected on standby servers.If a problem like this arises, it may not affect each individual index that is ordered using an affected collation, simply becauseindexed values might happen to have the same absolute ordering regardless of the behavioral inconsistency. SeeSection 23.1 andSection 23.2 for further details about howPostgreSQL uses operating system locales and collations.
Structural inconsistencies between indexes and the heap relations that are indexed (when
heapallindexedverification is performed).There is no cross-checking of indexes against their heap relation during normal operation. Symptoms of heap corruption can be subtle.
Corruption caused by hypothetical undiscovered bugs in the underlyingPostgreSQL access method code, sort code, or transaction management code.
Automatic verification of the structural integrity of indexes plays a role in the general testing of new or proposedPostgreSQL features that could plausibly allow a logical inconsistency to be introduced. Verification of table structure and associated visibility and transaction status information plays a similar role. One obvious testing strategy is to call
amcheckfunctions continuously when running the standard regression tests. SeeSection 31.1 for details on running the tests.File system or storage subsystem faults where checksums happen to simply not be enabled.
Note that
amcheckexamines a page as represented in some shared memory buffer at the time of verification if there is only a shared buffer hit when accessing the block. Consequently,amcheckdoes not necessarily examine data read from the file system at the time of verification. Note that when checksums are enabled,amcheckmay raise an error due to a checksum failure when a corrupt block is read into a buffer.Corruption caused by faulty RAM, or the broader memory subsystem.
PostgreSQL does not protect against correctable memory errors and it is assumed you will operate using RAM that uses industry standard Error Correcting Codes (ECC) or better protection. However, ECC memory is typically only immune to single-bit errors, and should not be assumed to provideabsolute protection against failures that result in memory corruption.
When
heapallindexedverification is performed, there is generally a greatly increased chance of detecting single-bit errors, since strict binary equality is tested, and the indexed attributes within the heap are tested.
Structural corruption can happen due to faulty storage hardware, or relation files being overwritten or modified by unrelated software. This kind of corruption can also be detected withdata page checksums.
Relation pages which are correctly formatted, internally consistent, and correct relative to their own internal checksums may still contain logical corruption. As such, this kind of corruption cannot be detected withchecksums. Examples include toasted values in the main table which lack a corresponding entry in the toast table, and tuples in the main table with a Transaction ID that is older than the oldest valid Transaction ID in the database or cluster.
Multiple causes of logical corruption have been observed in production systems, including bugs in thePostgreSQL server software, faulty and ill-conceived backup and restore tools, and user error.
Corrupt relations are most concerning in live production environments, precisely the same environments where high risk activities are least welcome. For this reason,verify_heapam has been designed to diagnose corruption without undue risk. It cannot guard against all causes of backend crashes, as even executing the calling query could be unsafe on a badly corrupted system. Access tocatalog tables is performed and could be problematic if the catalogs themselves are corrupted.
In general,amcheck can only prove the presence of corruption; it cannot prove its absence.
F.1.4. Repairing Corruption#
No error concerning corruption raised byamcheck should ever be a false positive.amcheck raises errors in the event of conditions that, by definition, should never happen, and so careful analysis ofamcheck errors is often required.
There is no general method of repairing problems thatamcheck detects. An explanation for the root cause of an invariant violation should be sought.pageinspect may play a useful role in diagnosing corruption thatamcheck detects. AREINDEX may not be effective in repairing corruption.