ɯei ZddlZddlZddlmZddlmZmZmZmZm Z m Z m Z ddl Z ddlmcmcmcmcmZddlmZddlmZddlmZmZmZmZmZmZddlm Z ddl!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(dd l)m*Z*dd l+m,Z,m-Z-dd l.m/Z/dd l0m1Z1m2Z2dd l3m4Z4ddl5m6Z6m7Z7ddl8m9Z9ddl:m;Z;ejxdkrddlm=Z=nddl>m=Z=ee?Z@GddeZAGddeZBGddeZCGddZDGddZEdee9deAfdZFdee9deBfd ZGdee9d!eHd"eHd#eHdeCf d$ZIGd%d&e6ZJy)'N) getLogger)DictListLiteral NamedTupleOptionalUnionoverload)create_join_type)SnowflakeTable)DeleteMergeExpressionInsertMergeExpression TableDelete TableMerge TableUpdateUpdateMergeExpression)Sample)build_expr_from_python_valbuild_expr_from_dict_str_strbuild_expr_from_snowpark_column-build_expr_from_snowpark_column_or_python_valwith_src_positionDATAFRAME_AST_PARAMETERbuild_table_name)SnowparkClientExceptionMessages) add_api_callset_api_call_source)ColumnOrLiteral) publicapiTimeTravelConfig)Column) DataFrame _disambiguate)Row)TimestampTimeZone) )Iterablec0eZdZUdZeed<dZeeed<y) UpdateResultz,Result of updating rows in a :class:`Table`. rows_updatedNmulti_joined_rows_updated)__name__ __module__ __qualname____doc__int__annotations__r,rZ/var/www/html/glpi_dashboard/venv/lib/python3.12/site-packages/snowflake/snowpark/table.pyr*r*8s$6 x   r4r*ceZdZUdZeed<y) DeleteResultz,Result of deleting rows in a :class:`Table`. rows_deletedNr-r.r/r0r1r2r3r4r5r7r7As 6r4r7c0eZdZUdZeed<eed<eed<y) MergeResultz=Result of merging a :class:`DataFrame` into a :class:`Table`. rows_insertedr+r8Nr9r3r4r5r;r;GsGr4r;cNeZdZdZ d deededdfdZdee e fddfdZ d Z y) WhenMatchedClausea A matched clause for the :meth:`Table.merge` action. It matches all remaining rows in the target :class:`Table` that satisfy ``join_expr`` while also satisfying ``condition``, if it is provided. You can use :func:`functions.when_matched` to instantiate this class. Args: condition: An optional :class:`Column` object representing the specified condition. For example, ``col("a") == 1``. N condition _emit_astreturncJ| |jnd|_||_d|_yN _expression_condition_expr _condition_clauseselfr?r@s r5__init__zWhenMatchedClause.__init__[)9B8My44SW# r4 assignmentsc|jr7tjt|jtrddddt |j ||_|S)a Defines an update action for the matched clause and returns an updated :class:`WhenMatchedClause` with the new update action added. Args: assignments: A list of values or a ``dict`` that associates the names of columns with the values that should be updated. The value of ``assignments`` can either be a literal value or a :class:`Column` object. Example:: >>> # Adds a matched clause where a row in source is matched >>> # if its key is equal to the key of any row in target. >>> # For all such rows, update its value to the value of the >>> # corresponding row in source. >>> from snowflake.snowpark.functions import when_matched, lit >>> target_df = session.create_dataframe([(10, "old"), (10, "too_old"), (11, "old")], schema=["key", "value"]) >>> target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary") >>> target = session.table("my_table") >>> source = session.create_dataframe([(10, "new")], schema=["key", "value"]) >>> target.merge(source, (target["key"] == source["key"]) & (target["value"] == lit("too_old")), [when_matched().update({"value": source["value"]})]) MergeResult(rows_inserted=0, rows_updated=1, rows_deleted=0) >>> target.sort("key", "value").collect() # the value in the table is updated [Row(KEY=10, VALUE='new'), Row(KEY=10, VALUE='old'), Row(KEY=11, VALUE='old')] Note: An exception will be raised if this method or :meth:`WhenMatchedClause.delete` is called more than once on the same :class:`WhenMatchedClause` object. updatedeleter>)rHr$MERGE_TABLE_ACTION_ALREADY_SPECIFIED isinstancerrF)rJrMs r5rOzWhenMatchedClause.updatebse@ <<1VVdll,AB$  #   -T-A-A;O  r4c|jr7tjt|jtrddddt |j |_|S)a Defines a delete action for the matched clause and returns an updated :class:`WhenMatchedClause` with the new delete action added. Example:: >>> # Adds a matched clause where a row in source is matched >>> # if its key is equal to the key of any row in target. >>> # For all such rows, delete them. >>> from snowflake.snowpark.functions import when_matched >>> target_df = session.create_dataframe([(10, "old"), (10, "too_old"), (11, "old")], schema=["key", "value"]) >>> target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary") >>> target = session.table("my_table") >>> source = session.create_dataframe([(10, "new")], schema=["key", "value"]) >>> target.merge(source, target["key"] == source["key"], [when_matched().delete()]) MergeResult(rows_inserted=0, rows_updated=0, rows_deleted=2) >>> target.collect() # the rows are deleted [Row(KEY=11, VALUE='old')] Note: An exception will be raised if this method or :meth:`WhenMatchedClause.update` is called more than once on the same :class:`WhenMatchedClause` object. rOrPr>)rHrrQrRrr rFrJs r5rPzWhenMatchedClause.deletesb2 <<1VVdll,AB$  #   -T-A-AB  r4NT) r-r.r/r0rr!boolrKrstrrrOrPr3r4r5r>r>OsT EI!&)=A ($sO';"<(AT(T!r4r>cXeZdZdZ d deededdfdZdee e e e e ffddfdZ y) WhenNotMatchedClausea A not-matched clause for the :meth:`Table.merge` action. It matches all remaining rows in the target :class:`Table` that do not satisfy ``join_expr`` but satisfy ``condition``, if it is provided. You can use :func:`functions.when_not_matched` to instantiate this class. Args: condition: An optional :class:`Column` object representing the specified condition. Nr?r@rAcJ| |jnd|_||_d|_yrCrDrIs r5rKzWhenNotMatchedClause.__init__rLr4rMc"|jrtjddt|tr3t |j }t |j}n g}t |}t|j|||_|S)a5 Defines an insert action for the not-matched clause and returns an updated :class:`WhenNotMatchedClause` with the new insert action added. Args: assignments: A list of values or a ``dict`` that associates the names of columns with the values that should be inserted. The value of ``assignments`` can either be a literal value or a :class:`Column` object. Examples:: >>> # Adds a not-matched clause where a row in source is not matched >>> # if its key does not equal the key of any row in target. >>> # For all such rows, insert a row into target whose ley and value >>> # are assigned to the key and value of the not matched row. >>> from snowflake.snowpark.functions import when_not_matched >>> from snowflake.snowpark.types import IntegerType, StringType, StructField, StructType >>> schema = StructType([StructField("key", IntegerType()), StructField("value", StringType())]) >>> target_df = session.create_dataframe([(10, "old"), (10, "too_old"), (11, "old")], schema=schema) >>> target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary") >>> target = session.table("my_table") >>> source = session.create_dataframe([(12, "new")], schema=schema) >>> target.merge(source, target["key"] == source["key"], [when_not_matched().insert([source["key"], source["value"]])]) MergeResult(rows_inserted=1, rows_updated=0, rows_deleted=0) >>> target.sort("key", "value").collect() # the rows are inserted [Row(KEY=10, VALUE='old'), Row(KEY=10, VALUE='too_old'), Row(KEY=11, VALUE='old'), Row(KEY=12, VALUE='new')] >>> # For all such rows, insert a row into target whose key is >>> # assigned to the key of the not matched row. >>> target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary") >>> target.merge(source, target["key"] == source["key"], [when_not_matched().insert({"key": source["key"]})]) MergeResult(rows_inserted=1, rows_updated=0, rows_deleted=0) >>> target.sort("key", "value").collect() # the rows are inserted [Row(KEY=10, VALUE='old'), Row(KEY=10, VALUE='too_old'), Row(KEY=11, VALUE='old'), Row(KEY=12, VALUE=None)] Note: An exception will be raised if this method is called more than once on the same :class:`WhenNotMatchedClause` object. insertrY) rHrrQrRdictlistkeysvaluesrrF)rJrMr_r`s r5r\zWhenNotMatchedClause.insertsZ <<1VV0  k4 ( ((*+D+,,./FD+&F,T-A-A4P  r4rU)r-r.r/r0rr!rVrKr r(rrrWr\r3r4r5rYrYs^ EI!&)=A 8 /!:DoAU""(!#)  -KK-)   . &?1    ) )$$<<''@@(73D3DA!** =D(D $)yQ#~~)*C'#5 D"23r4ci}|jr)|j|jjt|jf|j |j dd|S)NFrzr{r@)rrO_asdictrqry_sessionrrJkwargss r5_copy_without_astzTable._copy_without_astgsa  # # MM$22::< = OO MM&*&E&E     r4ci}|jr)|j|jjt|jf|j |j |j jd|S)Nr)rrOrrqryrr ast_enabledrs r5__copy__zTable.__copy__tsk  # # MM$22::< = OO MM&*&E&Emm//     r4c|SrCr3rTs r5 __enter__zTable.__enter__s r4c$|jyrC) drop_table)rJexc_typeexc_valexc_tbs r5__exit__zTable.__exit__s  r4)seedsampling_methodr@fracnrrr"ctj|||r!|jdvrtd|dddlm}d}|r|j jj}t|jj|}|r||j_ |r||j_ |r||j_ |r||j _ |j#|j$t'|j j(|r|dvrt*j-dt/|j0||| } |j3|j j4j7|j j4j9| |j j4 |j j4 | S|xsd } |t;|dzn|d} |d|dnd } d|j<d| d| d| } |j j?| | }|j j@r%|j0jB|j0_!|S)aUSamples rows based on either the number of rows to be returned or a percentage of rows to be returned. Sampling with a seed is not supported on views or subqueries. This method works on tables so it supports ``seed``. This is the main difference between :meth:`DataFrame.sample` and this method. Args: frac: The percentage of rows to be sampled. n: The fixed number of rows to sample in the range of 0 to 1,000,000 (inclusive). Either ``frac`` or ``n`` should be provided. seed: Specifies a seed value to make the sampling deterministic. Can be any integer between 0 and 2147483647 inclusive. Default value is ``None``. sampling_method: Specifies the sampling method to use: - "BERNOULLI" (or "ROW"): Includes each row with a probability of p/100. Similar to flipping a weighted coin for each row. - "SYSTEM" (or "BLOCK"): Includes each block of rows with a probability of p/100. Similar to flipping a weighted coin for each block of rows. This method does not support fixed-size sampling. Default is ``None``. Then the Snowflake database will use "ROW" by default. Note: - SYSTEM | BLOCK sampling is often faster than BERNOULLI | ROW sampling. - Sampling without a seed is often faster than sampling with a seed. - Fixed-size sampling can be slower than equivalent fraction-based sampling because fixed-size sampling prevents some query optimization. - Fixed-size sampling doesn't work with SYSTEM | BLOCK sampling. ) BERNOULLIROWSYSTEMBLOCKz'sampling_method' value zA must be None or one of 'BERNOULLI', 'ROW', 'SYSTEM', or 'BLOCK'.rMockServerConnectionN)rrzf[Local Testing] SYSTEM/BLOCK sampling is not supported for Local Testing, falling back to ROW sampling)probability_fraction row_countrrrr|gY@z ROWSz SEED ()zSELECT * FROM z SAMPLE z (z) )"r"_validate_sample_inputupper ValueError#snowflake.snowpark.mock._connectionrrrrrr table_samplerrnumrr _set_ast_refdfrR_conn_loggerwarningr_plan _with_planrrcreate_select_snowflake_planrWrysqlreduce_describe_query_enabled _metadata)rJrrrrr@rstmtr sample_plansampling_method_textfrac_or_rowcount_text seed_textsql_textnew_dfs r5samplez Table.samples>@ ((q1 446?  *?*;;|}  M ==++002D#DII$:$:DAC15((. ! !%,;##)   cff % dmm))+? @"55|! K?? ''??--11NN#dmm.E.EO"]]44 @ #  /4"595ED5L 1aSPU;)-)9gdV1%r #DOO#4H=Q|t| j|||j | j |t#| j$||| _|jjj)||jjj+|\} |t,<|j/t1|j2|jD cic].\} } t5| j6t5j8| 0c} } | |j6nd|r$t;||t=dgdj>nd|}tA|d|jBd||tDjFjHjJjLd|}|r tO|S|Scc} } w) aT Updates rows in the Table with specified ``assignments`` and returns a :class:`UpdateResult`, representing the number of rows modified and the number of multi-joined rows modified. Args: assignments: A ``dict`` that associates the names of columns with the values that should be updated. The value of ``assignments`` can either be a literal value or a :class:`Column` object. condition: An optional :class:`Column` object representing the specified condition. It must be provided if ``source`` is provided. source: An optional :class:`DataFrame` that is included in ``condition``. It can also be another :class:`Table`. statement_params: Dictionary of statement level parameters to be set while executing this action. block: A bool value indicating whether this function will wait until the result is available. When it is ``False``, this function executes the underlying queries of the dataframe asynchronously and returns an :class:`AsyncJob`. Examples:: >>> target_df = session.create_dataframe([(1, 1),(1, 2),(2, 1),(2, 2),(3, 1),(3, 2)], schema=["a", "b"]) >>> target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary") >>> t = session.table("my_table") >>> # update all rows in column "b" to 0 and all rows in column "a" >>> # to the summation of column "a" and column "b" >>> t.update({"b": 0, "a": t.a + t.b}) UpdateResult(rows_updated=6, multi_joined_rows_updated=0) >>> t.sort("a", "b").collect() [Row(A=2, B=0), Row(A=3, B=0), Row(A=3, B=0), Row(A=4, B=0), Row(A=4, B=0), Row(A=5, B=0)] >>> # update all rows in column "b" to 0 where column "a" has value 1 >>> target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary") >>> t.update({"b": 0}, t["a"] == 1) UpdateResult(rows_updated=2, multi_joined_rows_updated=0) >>> t.sort("a", "b").collect() [Row(A=1, B=0), Row(A=1, B=0), Row(A=2, B=1), Row(A=2, B=2), Row(A=3, B=1), Row(A=3, B=2)] >>> # update all rows in column "b" to 0 where column "a" in this >>> # table is equal to column "a" in another dataframe >>> target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary") >>> source_df = session.create_dataframe([1, 2, 3, 4], schema=["a"]) >>> t.update({"b": 0}, t["a"] == source_df.a, source_df) UpdateResult(rows_updated=6, multi_joined_rows_updated=0) >>> t.sort("a", "b").collect() [Row(A=1, B=0), Row(A=1, B=0), Row(A=2, B=0), Row(A=2, B=0), Row(A=3, B=0), Row(A=3, B=0)] N7condition should also be provided if source is providedleftrdr Table.updaterr data_typer3)(rrrrr table_updaterritemsrMadd_1r_2rr?rrrrevalflushrrrryr!rE_to_exprr#r rr_internal_collect_with_tag snowflakesnowpark async_job_AsyncResultTypeUPDATErg)rJrMr?rrrr@rrrkvt_rresults r5rOz Table.updatesKt % IH I% ==++002D#DII$:$:DAC   cff %&'--/KDAq++-AADA!$$JK$/ yI!##CJJ/+,S-A-ACSTCI MM $ $ ) )$ /261I1I1O1OPT1U .Av-. !, 1 1 311I))6??1+==*3)> %%DdF,>'s3I-cyrCr3rJr?rrrr@s r5rPz Table.deletek r4cyrCr3rs r5rPz Table.deletexrr4c |r |Jdi}d}|r|jjj}t|jj |}|j |j|t|j|||j |j|t|j|||_ |jjj||jjj|\} |t <|j#t%|j&| |j(nd|r$t+||t-dgdj.nd|} t1| d| j2d||t4j6j8j:j<d|} |r t?| S| S) aU Deletes rows in a Table and returns a :class:`DeleteResult`, representing the number of rows deleted. Args: condition: An optional :class:`Column` object representing the specified condition. It must be provided if ``source`` is provided. source: An optional :class:`DataFrame` that is included in ``condition``. It can also be another :class:`Table`. statement_params: Dictionary of statement level parameters to be set while executing this action. block: A bool value indicating whether this function will wait until the result is available. When it is ``False``, this function executes the underlying queries of the dataframe asynchronously and returns an :class:`AsyncJob`. Examples:: >>> target_df = session.create_dataframe([(1, 1),(1, 2),(2, 1),(2, 2),(3, 1),(3, 2)], schema=["a", "b"]) >>> target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary") >>> t = session.table("my_table") >>> # delete all rows in a table >>> t.delete() DeleteResult(rows_deleted=6) >>> t.collect() [] >>> # delete all rows where column "a" has value 1 >>> target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary") >>> t.delete(t["a"] == 1) DeleteResult(rows_deleted=2) >>> t.sort("a", "b").collect() [Row(A=2, B=1), Row(A=2, B=2), Row(A=3, B=1), Row(A=3, B=2)] >>> # delete all rows in this table where column "a" in this >>> # table is equal to column "a" in another dataframe >>> target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary") >>> source_df = session.create_dataframe([2, 3, 4, 5], schema=["a"]) >>> t.delete(t["a"] == source_df.a, source_df) DeleteResult(rows_deleted=4) >>> t.sort("a", "b").collect() [Row(A=1, B=1), Row(A=1, B=2)] Nrrrdrz Table.deleterr3) rrrrr table_deleterrrr?rrrrrrrrrryrEr#r rrrrrrrDELETEri) rJr?rrrr@rrrrrrs r5rPz Table.deletesh % IH I% ==++002D#DII$:$:DAC   cff %$/ yI!##CJJ/+,S-A-ACSTCI MM $ $ ) )$ /261I1I1O1OPT1U .Av-. )2)> %%DdF,>r4 join_exprclausescyrCr3rJrrrrrr@s r5mergez Table.mergerr4cyrCr3rs r5rz Table.mergerr4c  d\}}} g} |D]k} t| tr t| jtrd}n!d} nt| trd}n t d| j | jmi} d} |r|jjj} t| jj| }|j|j|j|jt!|j"||D]}||j|j$j'}t|tr^t|jtr|j(j+|jj,}|h|j/D]U\}}|j(j0j'}t!|j2|t!|j4|W|j6t!|j(j8|j6-t|jt:sI|j<j+|j6qt!|j<j8|j6t|tr2t|jt>s|j@j+|jjBK|jjBD]2}|j@jDj'}t!||4|jjFK|jjFD]2}|j@jHj'}t!||4|j6t!|j@j8|j6t tK|d|tM|jN|||_(|jjjS| |jjjU| \}| tV<|jYt[|j\t_||tadgdjb|jd| | }tg|d |jhd||tjjljnjpjrd | }d d l:m;}|s5t|jjx|s||_=||_>| |_?|rt|||| S|S)at Merges this :class:`Table` with :class:`DataFrame` source on the specified join expression and a list of matched or not-matched clauses, and returns a :class:`MergeResult`, representing the number of rows inserted, updated and deleted by this merge action. See `MERGE `_ for details. Args: source: A :class:`DataFrame` to join with this :class:`Table`. It can also be another :class:`Table`. join_expr: A :class:`Column` object representing the expression on which to join this :class:`Table` and ``source``. clauses: A list of matched or not-matched clauses specifying the actions to perform when the values from this :class:`Table` and ``source`` match or not match on ``join_expr``. These actions can only be instances of :class:`WhenMatchedClause` and :class:`WhenNotMatchedClause`, and will be performed sequentially in this list. statement_params: Dictionary of statement level parameters to be set while executing this action. block: A bool value indicating whether this function will wait until the result is available. When it is ``False``, this function executes the underlying queries of the dataframe asynchronously and returns an :class:`AsyncJob`. Example:: >>> from snowflake.snowpark.functions import when_matched, when_not_matched >>> from snowflake.snowpark.types import IntegerType, StringType, StructField, StructType >>> schema = StructType([StructField("key", IntegerType()), StructField("value", StringType())]) >>> target_df = session.create_dataframe([(10, "old"), (10, "too_old"), (11, "old")], schema=schema) >>> target_df.write.save_as_table("my_table", mode="overwrite", table_type="temporary") >>> target = session.table("my_table") >>> source = session.create_dataframe([(10, "new"), (12, "new"), (13, "old")], schema=schema) >>> target.merge(source, (target["key"] == source["key"]) & (target["value"] == "too_old"), ... [when_matched().update({"value": source["value"]}), when_not_matched().insert({"key": source["key"]})]) MergeResult(rows_inserted=2, rows_updated=1, rows_deleted=0) >>> target.sort("key", "value").collect() [Row(KEY=10, VALUE='new'), Row(KEY=10, VALUE='old'), Row(KEY=11, VALUE='old'), Row(KEY=12, VALUE=None), Row(KEY=13, VALUE=None)] )FFFTzHclauses only accepts WhenMatchedClause or WhenNotMatchedClause instancesNz* is not a valid type for merge clause AST.rrdrrrrr)rjrkrlr3)ArRr>rHrrY TypeErrorappendrrrrr table_mergerrrrrrr merge_update_when_matched_clauseClear _assignmentsrupdate_assignmentsrrrGr?r merge_delete_when_matched_clauser$merge_insert_when_not_matched_clause_keys insert_keys_values insert_valuestyperrrrrrrrryr#r rrErrrrrrMERGErrr _inserted_updated_deletedro)rJrrrrrr@rjrkrl merge_exprscrrrrmatched_clauserMrrrrrrrs r5rz Table.mergesb&9"'7  *A!./aii)>?"G"GA34^   qyy ) * ==++002D#DII$9$94@C   cff %    + 9#-- S 6 $)B%([[__%6N!%):;%emm5JK*KKQQS*/--*D*DK*6,7,=,=,? !&DAq(6(W(W(j(j(n(n(p%&%R()a%&%R()a%& !& %//; M$2$S$S$]$]$)$4$4!"( 7LM*KKQQS$//; M$2$S$S$]$]$)$4$4!" $E+?@%emm5JK*OOUUW$}}22>).)<)<!XA(6([([(g(g(k(k(m%&%RRSUV$W !X %}}44@).)>)>!XA(6([([(i(i(m(m(o%&%RRSUV$W !X %//; M$2$W$W$a$a$)$4$4!" (#E{m+UVi6 p +,S-A-ACSTCI MM $ $ ) )$ /261I1I1O1OPT1U .Av-. dF,$>EC   cff % MM $ $ ) )$ /261I1I1O1OPT1U .Av-. dmm))+? @ :DMM   / / : :    6DMM  doo./  65 @9? @r4)NFNT)rArq)NN)T)&r-r.r/r0rrWrrVprotoBindrr1r datetimer%rKrrrrfloatrr rrr!r"r*rOr7rPr(r>rYr;rr __classcell__)rs@r5rqrqsCG*/*. C4?C#' $=ABF $C4C4>?C4$( C4 EJJ' C4  C4#7>#:;C4C=C4 C4E#x'8'8"89:C4!s,='=!>?C4 C4 C4C4J    !%V #)-VuoV C=V sm V "# VV VVp'+&*  6:  #./  F#  #  #4S>2           '+&*  6:  #./  F#  #  #4S>2       '    '+&* l? 6:l?#./l?F#l?# l? #4S>2 l?l?l? |:: ;l?l?\'+&*  6:  F#  #  #4S>2           '+&*  6:  F#  #  #4S>2       '    '+&*\? 6:\?F#\?#\? #4S>2 \?  \?\? |:: ;\?\?|6:      % 13G GHI  #4S>2           6:      % 13G GHI  #4S>2       '    6:k k k % 13G GHI k #4S>2 k k k  {99 :k k Z@D@D@@r4rq)Ksysrloggingrtypingrrrrrr r snowflake.snowparkr4snowflake.snowpark._internal.proto.generated.ast_pb2r _internalr generatedast_pb26snowflake.snowpark._internal.analyzer.binary_plan_noder 9snowflake.snowpark._internal.analyzer.snowflake_plan_noder rYrgrirVrorqr3r4r5r8s<  MMMDDDSTIWTC-A&6 v( H  : : *^^BKK\)T#Y)<) )T#Y)<) B s) B# B.2 B=A B B u @Iu @r4