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Both read paths funnel through here: the paginated path reads the ``ptu_flat_cost`` column straight off the row, and the aggregated path reads the SUM() alias. Rows an operator accrued during an earlier opt-in stay in the table, so the gate lives on the read rather than on the query that produced the rows. The row is checked before the flag because this runs once per metric accumulation, and a record fans out across roughly a dozen breakdowns. The flag reads through the secret manager, uncached, so consulting it for every accumulation put thousands of lookups on a shared endpoint that made none before. Only a row actually carrying flat cost, which is a sentinel row, reaches it now. ptu_flat_costN)getattrr)rraws r9_reported_flat_costrs+$7>3C  * , Jr;existing_metricsc|jxsd}|jxsd}|xj|jxsdz c_|xjt |z c_|xj|z c_|xj|z c_|xj ||zz c_|xj |j xsdz c_|xj|jxsdz c_|xj|jxsdz c_|xj|jxsdz c_ |xj|jxsdz c_ |xj|jxsdz c_ |xj|jxsdz c_ |xj|jxsdz c_ |xj|jxsdz c_|xj|jxsdz c_|S)a Update metrics with new record data. Rollup rows can carry None for numeric fields when SUM() spans zero rows (e.g. a key with no spend), so coalesce to 0 before accumulating to avoid a TypeError. Mirrors the handling in ``_record_to_spend_metrics``. rr)rMrOrQ flat_costr total_tokensrSrUrXrZr\r^r`rbrdrf)rrrMrOs r9update_metricsrs"//41M%77<1fll1c1"5f"==""m3"&&*;;&!!]5F%FF!,,0N0N0SRSS,00F4V4V4[Z[[0--1P1P1UTUU-..&2R2R2WVWW.11V5X5X5]\]]1;;55:;--1P1P1UTUU-!!V%8%8%=A=!((F,F,F,K!K($$(>(>(C!C$ r;tagc|sy|jj}|jdxs|jdS)z>Determine whether a tag should be treated as a User-Agent tag.Fz user-agent:z user agent:)striplower startswith)rnormalized_tags r9_is_user_agent_tagrs> IIK--/N  $ $] 3 _~7P7PQ^7__r;recordsc$i}|D]]}t|dd}|st|dd}t|r,|j|}||j|jkDsY|||<_t }|j D]}t |||S)z Deduplicate spend metrics for tags using request_id, ignoring User-Agent prefixed tags. Each unique request_id contributes at most one record (the tag with max spend) to metadata. request_idNr)rrgetrQr!valuesr)rdeduped_recordsrr tag_value current_bestmetadata_metricss r9compute_tag_metadata_totalsrs ;=O 1!(t!D  FE40 i ( &**:6  6<<,2D2D#D*0OJ ' 1+n!((*1'01 r;entity_metadata_fieldr~c8|r|j|nd}||SiS)zXThe metadata payload for one entity breakdown bucket, empty when the caller passed none.N)r)rr~storeds r9_entity_metadatars+ =R)--i8W[F'6/R/r; breakdownmodel_metadataprovider_metadataapi_key_metadataentity_id_fieldc (|jtk(}|r|jxs|jn |j}|r@||jvr2t t |j|i|j|<|rXt|j|j||j|_ |s |j|j|jvrtt t|j|jijdd|j|jijdd|j|j|j<t|j|j|jj||j|j|j_ |jxs |j} | r@| |jvr2t t |j| i|j| <| rXt|j| j||j| _ |s |j|j| jvrtt t|j|jijdd|j|jijdd|j| j|j<t|j| j|jj||j| j|j_ |jr|j|jvr,t t i|j|j<t|j|jj||j|j_ |j|j|jjvrtt t|j|jijdd|j|jijdd|j|jj|j<t|j|jj|jj||j|jj|j_ |s|j xsd} | |j"vr2t t |j| i|j"| <t|j"| j||j"| _ |j|j"| jvrtt t|j|jijdd|j|jijdd|j"| j|j<t|j"| j|jj||j"| j|j_ |j$r|j$|j&vr,t t i|j&|j$<t|j&|j$j||j&|j$_ |j|j&|j$jvrtt t|j|jijdd|j|jijdd|j&|j$j|j<t|j&|j$j|jj||j&|j$j|j_ |s|j|j(vrtt t|j|jijdd|j|jijdd|j(|j<t|j(|jj||j(|j_ |rt+||d} | r| nd} | |j,vr,t t t/|| |j,| <t|j,| j||j,| _ |s |j|j,| jvrtt t|j|jijdd|j|jijdd|j,| j|j<t|j,| j|jj||j,| j|j_ |S)a!Updates breakdown metrics for a single record using the existing update_metrics function. PTU sentinel rows (api_key == PTU_SENTINEL_API_KEY) add their flat cost to every parent bucket but never appear as an api_key row, and are kept out of the per-request provider breakdown.metricsmetadatarpNrqrprqunknown Unassigned)r=rrCr@modelsrr!rrrapi_key_breakdownrr model_groupsrH mcp_serversrE providersrJ endpointsapi_keysrentitiesr) rrrrrrris_ptu_sentinel model_keymodel_group_keyprovider entity_values r9update_breakdown_metricsrs} $^^/CCO @O**:fllTZT`T`IYi&6&66&8 N#'' 26' #.F >>!6!6v7V7V!W!i!i i%(N("2"6"6v~~r"J"N"N{\`"a 0 4 4V^^R H L LYX\ ]  ! !&"A"A B T TU[UcUc d#  ! !&"A"A B T TU[UcUc d l l   f==>PP NN   44A  9.. .,>$*..x<-I   )1?y?R?RS[?\?d?dfl0m H%- >>!4!4X!>!P!P PNc$$.226>>2FJJ;X\],00DHHTXYOI   ) ; ;FNN KSa    ) ; ;FNN K S S S H%77GO  ??)"5"5 53E$4I   08F    0 8 8&8 FOO,4 >>!4!4V__!E!W!W WUj$$.226>>2FJJ;X\],00DHHTXYVI   0 B B6>> RZh    0 B B6>> R Z Z Z FOO,>>v~~NV  >>!3!3 31F$$.226>>2FJJ;X\],00DHHTXY2I  v~~ .6DIDVDVW]WeWeDfDnDnpv5w 6>>*2v= '3| y11 1/A$)*?N0I  | ,4B)BTBTUaBbBjBjlr3s <(0~~Y%7%7 %E%W%WWUj(N("2"6"6v~~r"J"N"N{\`"a 0 4 4V^^R H L LYX\ ]V ""<0BB6>>RZh""<0BB6>>RZZZI  | , > >v~~ N V r; prisma_clientrcKt|jjddt|iid{}|Dcic]&}|j|j |j d(}}|t|jz }|r} t|jjddt|iiddid{}|D]7}|j|vs|j |j d||j<9 |S|S7cc}w7L#t$r+}tjd t||Yd}~|Sd}~wwxYww) zGet api key metadata, falling back to deleted keys table for keys not found in active table. This ensures that key_alias and team_id are preserved in historical activity logs even after a key is deleted or regenerated. tokeninwhereNr deleted_atdesc)rorderzH57N$ NL ! 7 $)! gt $+// Y '*1 Y ' i &15y0A _ -1990E _ -/33ORH gs #)G 23,3) r;rrc g}g}d |jd |j| dz |jd |j| dz |t|trx|rddj fdt t |D} |jd|d| d|j | t |z n?|jd n-|jd|d |j| dz |rcdj fd t t |D} |jd|d | d|j | t |z |r*|jd |j| dz t|tru|radj fdt t |D} |jd| d|j | t |z n>|jd n,|r*|jd |j| dz dj||fS)zFBuild the WHERE clause and $N params shared by the aggregated queries.z date >= $z date <= $z, c3.K|] }d|zyw$Nr6.0ips r9 z1_build_aggregated_where_clause..`s(T1QUG(T"z" IN ()FALSEz" = $c3.K|] }d|zywrr6rs r9rz1_build_aggregated_where_clause..ms U1QUG Urz " NOT IN (z model = $c3.K|] }d|zywrr6rs r9rz1_build_aggregated_where_clause..{s$NQqQ[$Nrz api_key IN (z api_key = $z AND )appendrrxjoinrangerextend) rr~rrr@r=rsql_conditions sql_params placeholdersrs @r9_build_aggregated_where_clauserDsZ(*N#%J AIaS/*n%FAIaS/*l#FA i &#yy(TeC N>S(TT %%/):&a&PQ!!),S^#%%g.  ! !Ao%6eA3"? @   i ( FAyy UeC@R'%!- L*,  #: . / 0jn7+IZ j  r;c tj|} | td|t|||| \} } t ||| | |||\} }d|dt |d| d| d|d|d }||fS) u Per-entity companion to _build_aggregated_sql_query. Two rollup levels over the same WHERE clause — (date, entity) and (date, entity, api_key) — told apart by GROUPING(api_key): 1 when the api_key column is rolled up, 0 when it is part of the key. rr z SELECT "z" AS entity_id, date, api_key, GROUPING(api_key) AS api_key_rolled, SUM(spend)::float AS spend, r r z6 GROUP BY GROUPING SETS ( (date, "z"), (date, "z", api_key) ) r rs r9_build_entity_rollup_sql_queryrs&*--j9H/ |<==#=H57N$ NL >'%!- L*  #: . / 0jn$%&$%&1I8 j  r;c i}i}g}t}i}|D]l} | j} | |vrttd|| <t|| d| || d<t || d| ||||||| d<t|| }n|j D]J\} } |j ttj| dj| d| dL|jdd ||d S) Nrrrr)rr%Y-%m-%dr:rrc|jSr5r:xs r9z/_aggregate_spend_records_sync..g qvvr;Tkeyreverservrw) r!r:rrritemsrrrstrptimesort) rrrrrrrv total_metrics grouped_datardate_strdatas r9_aggregate_spend_records_syncr*:s4;=N=?+-G NM24L>;; < ''>-/&L " -;<;QR[;\^d,e Xy).F  "; /    +"7/ X{+'}f= )>,',,. $ &&x<AACY{+    LL%tL4- 88r;c K|Dchc]/}|js|jtk7s$|j1}}i}|rt||d{}tjt ||||d{Scc}w7/7 w)zAggregate rows into DailySpendData list and total metrics. The per-row loop is offloaded to a worker thread via asyncio.to_thread so a large result set doesn't peg the event loop. N)rrrr)r=rrasyncio to_threadr*)rrrrrrrs r9_aggregate_spend_recordsr.ls&-!!6>>UiCi!H!57!5mX!NN""%)'3  ! O s7BA;A;A;BB%B6B7 BB_GROUP_GRAND_TOTAL? _GROUP_DATE_GROUP_DATE_API_KEY/_GROUP_DATE_MODEL_GROUP_DATE_MODEL_API_KEY7_GROUP_DATE_MODEL_GROUP_GROUP_DATE_MODEL_GROUP_API_KEY;_GROUP_DATE_PROVIDER_GROUP_DATE_PROVIDER_API_KEY=_GROUP_DATE_MCP_GROUP_DATE_MCP_API_KEY>_GROUP_DATE_ENDPOINT_GROUP_DATE_ENDPOINT_API_KEYc|jxsd}|jxsd}t|jxsdt |||||z|j xsd|j xsd|jxsd|jxsd|jxsd|jxsd|jxsd|jxsd|jxsd|jxsdS)zBuild a SpendMetrics directly from one already-aggregated rollup row. SUM() over zero rows is SQL NULL, so rollup rows (notably the grand-total row, which Postgres emits even on an empty match) can carry None values. rr)rQrrMrOrrSrUrXrZr\r^r`rbrdrf)rMrOr!rQrrSrUrXrZr\r^r`rbrdrf)rrMrOs r9_record_to_spend_metricsrJs "//41M%77<1 ll!c%f-#+"%66 & > > C!$*$F$F$K!!'!@!@!EA"("B"B"Ga%+%H%H%MA/5/\/\/a`a!'!@!@!EA((-A"66;!..3! r;c||j|i}t|jd|jdS)Nrprqr)rr)rr=metas r9 _key_metadatarMs3"&&w3D +!6@S TTr;c t}idtdtffd }dtttfdtdtddfd}dtttfd td tdtddf fd }|D]p}|j }t |}|jtk(} |tk(r|}:|tk(r|||jd<Y||jd } |tk(rI|js| rt|t|j | j|j<|t k(r,|j"s|| j$|j"||t&k(rK|j"s |js| r|| j$|j"|j|I|t(k(r.|j*s`|| j,|j*||t.k(rK|j*s|js| r|| j,|j*|j||t0k(r:|j3ddi} |j4xsd} || j6| | |t8k(rC|js.| r2|j4xsd} || j6| |j|c|t:k(r.|j<sz|| j>|j<||t@k(rG|j<s|js|| j>|j<|j||tBk(r.|jDs|| jF|jD|!|tHk(s,|jDs:|jsH|| jF|jD|j|sjKD cgc];\} }tMtOjP| dj|d|d =}} }|jSdd||dScc}} w)uhBuild the response from rollup rows produced by the GROUPING SETS query. 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We dispatch the row's pre-aggregated metrics straight into the matching bucket — no per-row summing in Python and no nested update_metrics calls. r(r3cbj|}|ttd}||<|S)Nr)rr!r)r(bucketr's r9 ensure_datez:_aggregate_grouping_sets_records_sync..ensure_dates7%1%5%5h%? >!->N>PQF%+L " r;targetr rNcZ|j|}|t|i||<y||_yNr)rrr)rRr rexistings r9assign_metric_with_metadatazJ_aggregate_grouping_sets_records_sync..assign_metric_with_metadatas- **S/  ,WrJF3K&H r; parent_keyr=c|j|}|tti}|||<t|t ||j |<yrT)rrr!rrMr)rRrWr=rparentrs r9assign_api_key_breakdownzG_aggregate_grouping_sets_records_sync..assign_api_key_breakdownsR J' >' LF!'F: ,Am4Dg&N-   )r;rrrr)updaterrrc|jSr5rrs r9rz7_aggregate_grouping_sets_records_sync..8rr;Trr")*r!rkrdictrr{rJr=rr0r2r:r4rrMrr6r@rr8r:rCrr<r> model_copyrErr@rBrHrrDrFrJrrHr#rrr$r%)rrr&rQrVrZrlevelrrrprovider_metricsrr(r)rvr's ` @r9%_aggregate_grouping_sets_records_syncras!NM24Lck'D6H1H,I'PS'^j'os'  S,,-          Dh""*62 ..,@@ & &#M  K 29K $Y /  ,[9 ' '~~o5J#*+;V^^L6 ""6>>2' '||+I,<,YH ' (;(;XGW X 2 2~~o!55B()<)>())33NN  * *+I,?,?RYZ 2 26>>()<)#B4B5 BBpage page_sizemetadata_metrics_funcresolve_entity_metadatac K|'tddtjji||ttj ddi t ||||||| | | }t|j|j|d{}t|j|j|dd id d ig| d z | z| d{}|}|i|xsi||d{}t||||d{}|d}| r| |}t|dtd&id|jd|jd|j d|j"d|j$d|j&d|j(d|j*d|j,d|j.d|j0d|j2d|j4d|j6d|j8d | d!| | z d"| | z|k#S7r787 7 #t:$r;}t=j>d$|ttj@dd%|id}~wwxYww)'a6Common function to get daily activity for any entity type. ``resolve_entity_metadata`` lets a caller resolve entity metadata from the rows actually on the page (e.g. user_id -> user_email) instead of fetching the whole entity table upfront, which matters when the entity set is unbounded. Nerror status_codedetail&Please provide start_date and end_date) rr~rrr@r=rrrrr:ridascr)rrskiptake)rrrrrwrv total_spendtotal_flat_costtotal_prompt_tokenstotal_completion_tokensrtotal_api_requeststotal_successful_requeststotal_failed_requeststotal_cache_read_input_tokens!total_cache_creation_input_tokenstotal_compression_saved_tokenstotal_compression_savings_spend"total_prompt_caching_savings_spend,total_gateway_injected_caching_savings_spendtotal_autorouter_savings_spendre total_pageshas_morervrz!Error fetching daily activity: %sFailed to fetch analytics: r6)!rrdb_not_connected_errorvaluerHTTP_400_BAD_REQUESTrrdbcountrr.r rrQrrMrOrrbrdrfrSrUrXrZr\r^r`rr exceptionHTTP_500_INTERNAL_SERVER_ERROR)rrrr~rrrr@r=rerfrrgrrrhr total_countdaily_spend_dataresolved_entity_metadata aggregatedrrs r9get_daily_activityrPs6.EEKKL  X-33EF  S "9+!1$;$; # )0 0@0@*(M(S(SZj(S(k"k ELML\L\^hDiDsDs" u (i'EtE ? $9 " .((.B(01ABB( $ #;'$+": #  &h/ 45EF .y)',22 0 : :%5$B$B)9(J(J  .::  $4#@#@ +;*N*N'7&F&F/?.V.V3C2^2^0@/X/X1A0Z0Z4D3`3`>N=t=t0@/X/X !" +li78#$*k9%  Q#l? C F  &&'JAN==:1#>?   ssA I=H G= '$)*?KF#)HY *3/   $FN [[S[[,@@4I-8H#++*V5F $ $S[[ 1'Ms#D%include_entity_breakdownc K|'tddtjji||ttj ddi t |||||||| | |  \} }| rt|||||||| | |  nd}|Ztj|jj| g||jj|dg|dd{n&|jj| g|d{df\}}|xsgDcgc] }td%i|}}t|| d{}|retd |D}td |D}|rt!||d{ni}tj"t$|d ||| d{t'|d t)d%id|dj*d|dj,d|dj.d|dj0d|dj2d|dj4d|dj6d|dj8d|dj:d|dj<d|dj>d|dj@d|djBd|djDd|djFddddd d!"S77cc}w77N7&#tH$r;}tKjLd#|ttjNdd$|id}~wwxYww)&a(Aggregated variant that returns the full result set (no pagination). Uses SQL GROUP BY to aggregate rows in the database rather than fetching all individual rows into Python. This collapses rows across entities (users/teams/orgs), reducing ~150k rows to ~2-3k grouped rows. include_entity_breakdown runs a small companion rollup query and folds `breakdown.entities` onto the response, as entity-scoped views like Team Usage need. Matches the response model of the paginated endpoint so the UI does not need to transform. 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