Column-level conflict detection#
By default, conflicts are resolved at row level. When changes from two nodes conflict, either the local or remote tuple is selected and the other is discarded. For example, commit timestamps for the two conflicting changes might be compared and the newer one kept. This approach ensures that all nodes converge to the same result and establishes commit-order-like semantics on the whole cluster.
However, in some cases it might be appropriate to resolve conflicts at the column level rather than the row level.
Consider a simple example, in which table t has two integer columns a
and b and a single row (1,1) . On one node execute:
UPDATE t SET a = 100
On another node, before receiving the preceding UPDATE ,
concurrently execute:
UPDATE t SET b = 100
This sequence results in an UPDATE-UPDATE conflict. With the
update_if_newer conflict resolution, the commit timestamps are
compared, and the new row version is kept. Assuming the second node
committed last, the result is (1,100) , which effectively discards
the change to column a.
For many use cases, this behavior is the desired and expected. However, for some use cases, this might be an issue. Consider, for example, a multi-node cluster where each part of the application is connected to a different node, updating a dedicated subset of columns in a shared table. In that case, the different components might conflict and overwrite changes.
For such use cases, it might be more appropriate to resolve conflicts on
a given table at the column level. To achieve that, PGD tracks the
timestamp of the last change for each column separately and uses that to
pick the most recent value, essentially performing update_if_newer .
Applied to the previous example, the result is (100,100) on both
nodes, despite neither of the nodes ever seeing such a row.
When thinking about column-level conflict resolution, it can be useful to see tables as vertically partitioned, so that each update affects data in only one slice. This approach eliminates conflicts between changes to different subsets of columns. In fact, vertical partitioning can even be a practical alternative to column-level conflict resolution.
Column-level conflict resolution requires the table to have
REPLICA IDENTITY FULL . The bdr.alter_table_conflict_detection
function checks that and fails with an error if this setting is missing.
Enabling and disabling column-level conflict resolution#
The bdr.alter_table_conflict_detection() function manages column-level conflict resolution.
Example#
This example creates a table test_table and then enables
column-level conflict resolution on it:
db=# CREATE TABLE my_app.test_table (id SERIAL PRIMARY KEY, val INT);
CREATE TABLE
db=# ALTER TABLE my_app.test_table REPLICA IDENTITY FULL;
ALTER TABLE
db=# SELECT bdr.alter_table_conflict_detection(
db(# my_app.test_table::regclass,
db(# column_modify_timestamp, cts);
alter_table_conflict_detection
- -------------------------------
t
db=# \d my_app.test_table
The function adds a cts column as specified in the function call. It
also creates two triggers (BEFORE INSERT and BEFORE UPDATE )
that are responsible for maintaining timestamps in the new column before
each change.
The new column specifies NOT NULL with a default value, which means
that ALTER TABLE ... ADD COLUMN doesn’t perform a table rewrite.
Note
Avoid using columns with the bdr.column_timestamps data type for other purposes, as doing so can have negative effects. For example, it switches the table to column-level conflict resolution, which doesn't work correctly without the triggers.
Listing table with column-level conflict resolution#
You can list tables having column-level conflict resolution enabled with
the following query. This query detects the presence of a column of type
bdr.column_timestamp .
SELECT nc.nspname, c.relname
FROM pg_attribute a
JOIN (pg_class c JOIN pg_namespace nc ON c.relnamespace = nc.oid)
ON a.attrelid = c.oid
JOIN (pg_type t JOIN pg_namespace nt ON t.typnamespace = nt.oid)
ON a.atttypid = t.oid
WHERE NOT pg_is_other_temp_schema(nc.oid)
AND nt.nspname = bdr
AND t.typname = column_timestamps
AND NOT a.attisdropped
AND c.relkind IN (r, v, f, p);
bdr.column_timestamps_create#
This function creates column-level conflict resolution. It’s called
within column_timestamp_enable .
Synopsis#
bdr.column_timestamps_create(p_source cstring, p_timestamp timestampstz)
Parameters#
p_source— The two options arecurrentorcommit.p_timestamp— Timestamp depends on the source chosen. Ifcommit, thenTIMESTAMP_SOURCE_COMMIT. Ifcurrent, thenTIMESTAMP_SOURCE_CURRENT.
DDL locking#
When enabling or disabling column timestamps on a table, the code uses DDL locking to ensure that there are no pending changes from before the switch. This approach ensures only conflicts with timestamps in both tuples or in neither of them are seen. Otherwise, the code might unexpectedly see timestamps in the local tuple and NULL in the remote one. It also ensures that the changes are resolved the same way (column-level or row-level) on all nodes.
Current versus commit timestamp#
An important decision is the timestamp to assign to modified columns.
By default, the timestamp assigned to modified columns is the current
timestamp, as if obtained from clock_timestamp . This is simple, and
for many cases it is correct (for example, when the conflicting rows
modify non-overlapping subsets of columns).
It can, however, have various unexpected effects:
The timestamp changes during statement execution. So, if an
UPDATEaffects multiple rows, each gets a slightly different timestamp. This means that the effects of concurrent changes might get “mixed” in various ways, depending on how the changes performed on different nodes interleave.The timestamp is unrelated to the commit timestamp. Using it to resolve conflicts means that the result isn’t equivalent to the commit order, which means it likely can’t be serialized.
Note
Statement and transaction timestamps might be added in the future, which will address issues with mixing effects of concurrent statements or transactions. Still, neither of these options can ever produce results equivalent to commit order.
You can also use the actual commit timestamp, although this feature is
considered experimental. To use the commit timestamp, set the last
parameter to true when enabling column-level conflict resolution:
SELECT bdr.column_timestamps_enable(test_table::regclass, cts, true);
You can disable it using bdr.column_timestamps_disable .
- Commit timestamps currently have restrictions that are explained in
Inspecting column timestamps#
The column storing timestamps for modified columns is maintained by triggers. Don’t modify it directly. It can be useful to inspect the current timestamps value, for example, while investigating how a conflict was resolved.
Three functions are useful for this purpose:
bdr.column_timestamps_to_text(bdr.column_timestamps)
This function returns a human-readable representation of the timestamp
mapping and is used when casting the value to text :
db=# select cts::text from test_table;
cts
- ----------------------------------------------------------------------------------------------------
{source: current, default: 2018-09-23 19:24:52.118583+02, map: [2 : 2018-09-23 19:25:02.590677+02]}
(1 row)
bdr.column_timestamps_to_jsonb(bdr.column_timestamps)
This function turns a JSONB representation of the timestamps mapping and
is used when casting the value to jsonb :
db=# select jsonb_pretty(cts::jsonb) from test_table;
jsonb_pretty
- --------------------------------------------------
{ +
"map": { +
"2": "2018-09-23T19:24:52.118583+02:00" +
}, +
"source": "current", +
"default": "2018-09-23T19:24:52.118583+02:00"+
}
(1 row)
bdr.column_timestamps_resolve(bdr.column_timestamps, xid)
This function updates the mapping with the commit timestamp for the
attributes modified by the most recent transaction if it already
committed. This matters only when using the commit timestamp. For
example, in this case, the last transaction updated the second attribute
(with attnum = 2 ):
test=# select cts::jsonb from test_table;
cts
- ---------------------------------------------------------------------------------------------------------------------------------------
{"map": {"2": "2018-09-23T19:29:55.581823+02:00"}, "source": "commit", "default": "2018-09-23T19:29:55.581823+02:00", "modified": [2]}
(1 row)
db=# select bdr.column_timestamps_resolve(cts, xmin)::jsonb from test_table;
column_timestamps_resolve
- ----------------------------------------------------------------------------------------------------------------------
{"map": {"2": "2018-09-23T19:29:55.581823+02:00"}, "source": "commit", "default": "2018-09-23T19:29:55.581823+02:00"}
(1 row)
Handling column conflicts using CRDT data types#
By default, column-level conflict resolution picks the value with a higher timestamp and discards the other one. You can, however, reconcile the conflict in different, more elaborate ways. For example, you can use CRDT types that allow merging the conflicting values without discarding any information.
Notes#
The attributes modified by an
UPDATEare determined by comparing the old and new row in a trigger. This means that if the attribute doesn’t change a value, it isn’t detected as modified even if it’s explicitly set. For example,UPDATE t SET a = adoesn’t markaas modified for any row. Similarly,UPDATE t SET a = 1doesn’t markaas modified for rows that are already set to1.For
INSERTstatements, there’s no old row to compare the new one to, so all attributes are considered to be modified, and they are assigned a new timestamp. This condition applies even for columns that weren’t included in theINSERTstatement and received default values. PGD can detect the attributes that have a default value but can’t know if it was included automatically or specified explicitly.
This situation effectively means column-level conflict resolution
doesn’t work for INSERT-INSERT conflicts even if the INSERT
statements specify different subsets of columns. The newer row has
timestamps that are all newer than the older row.
By treating the columns independently, it’s easy to violate constraints in a way that isn’t possible when all changes happen on the same node. Consider, for example, a table like this:
CREATE TABLE t (id INT PRIMARY KEY, a INT, b INT, CHECK (a > b));
INSERT INTO t VALUES (1, 1000, 1);
Assume one node does:
UPDATE t SET a = 100;
Another node concurrently does:
UPDATE t SET b = 500;
Each of those updates is valid when executed on the initial row and so
passes on each node. But when replicating to the other node, the
resulting row violates the CHECK (A > b) constraint, and the
replication stops until the issue is resolved manually.
The column storing timestamp mapping is managed automatically. Don’t specify or override the value in your queries, as the results can be unpredictable. (The value is ignored where possible.)
The timestamp mapping is maintained by triggers, but the order in which triggers execute matters. So if you have custom triggers that modify tuples and are executed after the
pgl_clcd_triggers, the modified columns aren’t detected correctly.When using regular timestamps to order changes or commits, the conflicting changes might have exactly the same timestamp because two or more nodes happened to generate the same timestamp. This risk isn’t unique to column-level conflict resolution, as it can happen even for regular row-level conflict resolution. The node id is the tie breaker in this situation. The higher node id wins. This approach ensures that the same changes are applied on all nodes.
A clock skew can occur between different nodes. It can induce somewhat unexpected behavior, discarding seemingly newer changes because the timestamps are inverted. However, you can manage clock skew between nodes using the parameters
bdr.maximum_clock_skewandbdr.maximum_clock_skew_action.
SELECT bdr.alter_node_group_config(group, ignore_redundant_updates := false);