Re: Timeline consistency using PQS
Could someone clarify how this property is used by Phoenix: phoenix.connection.consistency If I set it in hbase-site.xml, does phoenix utilize it for every query (even queries from PQS)? It's not documented on the website but it's defined in QueryServices.java: // consistency configuration setting public static final String CONSISTENCY_ATTRIB = "phoenix.connection.consistency"; And used in PhoenixConnection.java this.consistency = JDBCUtil.getConsistencyLevel(url, this.info, this.services.getProps().get("phoenix.connection.consistency", QueryServicesOptions.DEFAULT_CONSISTENCY_LEVEL)); On Thu, Jan 19, 2017 at 2:35 PM, Tulasi Paradarami < tulasi.krishn...@gmail.com> wrote: > Hi, > > Does PQS support HBase's timeline consistency (HBASE-10070)? > > Looking at the connection properties implementation within Avatica, I see > that following are defined: ["transactionIsolation", "schema", "readOnly", > "dirty", "autoCommit", "catalog"] but there's isn't a property defined for > setting consistency. > > org.apache.calcite.avatica.ConnectionPropertiesImpl.java: > @JsonCreator > public ConnectionPropertiesImpl( > @JsonProperty("autoCommit") Boolean autoCommit, > @JsonProperty("readOnly") Boolean readOnly, > @JsonProperty("transactionIsolation") Integer transactionIsolation, > @JsonProperty("catalog") String catalog, > @JsonProperty("schema") String schema) { > this.autoCommit = autoCommit; > this.readOnly = readOnly; > this.transactionIsolation = transactionIsolation; > this.catalog = catalog; > this.schema = schema; > } > >
Timeline consistency using PQS
Hi, Does PQS support HBase's timeline consistency (HBASE-10070)? Looking at the connection properties implementation within Avatica, I see that following are defined: ["transactionIsolation", "schema", "readOnly", "dirty", "autoCommit", "catalog"] but there's isn't a property defined for setting consistency. org.apache.calcite.avatica.ConnectionPropertiesImpl.java: @JsonCreator public ConnectionPropertiesImpl( @JsonProperty("autoCommit") Boolean autoCommit, @JsonProperty("readOnly") Boolean readOnly, @JsonProperty("transactionIsolation") Integer transactionIsolation, @JsonProperty("catalog") String catalog, @JsonProperty("schema") String schema) { this.autoCommit = autoCommit; this.readOnly = readOnly; this.transactionIsolation = transactionIsolation; this.catalog = catalog; this.schema = schema; }
Re: Moving column family into new table
I'll check when I'm on site tomorrow, but our (much smaller) local cluster is using the default hbase.hregion.max.filesize of 10 GB for HDP. hbase.hregion.majorcompaction is set to 7 days, so I'm sure it would have ran by now. What would be the best filesize limit? Cloudera suggests having 20-200 regions per RegionServer. Should I try increasing to 20 GB? Why does Cloudera also say that 5-10 GB is optimal...is it to achieve more regions? The Spark tasks don't get killed, some sort of RPC call gets timed out. Nothing appears in the YARN logs, and the Spark history server just has a state of "failed." We did small-scale testing of Phoenix+Spark first and fine-tuned as much as we could, which is how we settled on the current design. Query times seemed pretty predictable as we scaled up, and our use cases would still fit within the upper bounds of what we estimated for querying on more data. The direct queries in Phoenix aren't an issue at all, they're actually working pretty great, we're just facing a new problem with Spark+Phoenix. I'll check the on-site cluster and if I find any bugs, I'll be sure to report them! On Thu, Jan 19, 2017 at 1:28 PM, Josh Mahonin wrote: > It's a bit peculiar that you've got it pre-split to 10 salt buckets, but > seeing 400+ partitions. It sounds like HBase is splitting the regions on > you, possibly due to the 'hbase.hregion.max.filesize' setting. You should > be able to check the HBase Master UI and see the table details to see how > many regions there are, and what nodes they're located on. Right now, the > Phoenix MR / Spark integration basically assigns one partition per region. > > As a total guess, I wonder if somehow the first 380 partitions are > relatively sparse, and the bulk of the data is in the remaining 70 > partitions. You might be able to diagnose that by adding some logging in a > 'mapPartitions()' call. It's possible that running a major compaction on > that table might help redistribute the data as well. > > If you're seeing your task getting killed, definitely try dig into the > Spark executor / driver logs to try find a root cause. If you're using > YARN, you can usually get into the Spark history server, then check the > 'stdout' / 'stderr' logs for each executor. > > Re: architecture recommendations, it's possible that phoenix-spark isn't > the right tool for this job, though we routinely read / write billions of > rows with it. I'd recommend trying to start with a smaller subset of your > data and make sure you've got the schema, queries and HBase settings setup > the way you like, then add Spark into the mix. Then start adding a bit more > data, check results, find any bottlenecks, and tune as needed. > > If you're able to identify any issues specifically with Phoenix, bug > reports and patches are greatly appreciated! > > Best of luck, > > Josh > > > On Thu, Jan 19, 2017 at 12:30 PM, Mark Heppner > wrote: > >> Thanks for the quick reply, Josh! >> >> For our demo cluster, we have 5 nodes, so the table was already set to 10 >> salt buckets. I know you can increase the salt buckets after the table is >> created, but how do you change the split points? The repartition in Spark >> seemed to be extremely inefficient, so we were trying to skip it and keep >> the 400+ default partitions. >> >> The biggest issue we're facing is that as Spark goes through the >> partitions during the scan, it becomes exponentially slower towards the >> end. Around task 380/450, it slows down to a halt, eventually timing out >> around 410 and getting killed. We have no idea if this is something with >> Spark, YARN, or HBase, so that's why we were brainstorming with using the >> foreign key-based layout, hoping that the files on HDFS would be more >> compacted. >> >> We haven't noticed too much network overhead, nor have we seen CPU or RAM >> usage too high. Our nodes are pretty big, 32 cores and 256 GB RAM each, >> connected on a 10 GbE network. Even if our query is for 80-100 rows, the >> Spark job still slows to a crawl at the end, but that should really only be >> about 80 MB of data it would be pulling out of Phoenix into the executors. >> I guess we should have verified that the Phoenix+Spark plugin did achieve >> data locality, but there isn't anything that says otherwise. Even though it >> doesn't have data locality, we have no idea why it would progressively slow >> down as it reaches the end of the scan/filter. >> >> The images are converted to a NumPy array, then saved as a binary string >> into Phoenix. In Spark, this is fairly quick to convert the binary string >> back to the NumPy array. This also allows us to use GET_BYTE() from Phoenix >> to extract specific values within the array, without going through Spark at >> all. Do you have any other architecture recommendations for our use case? >> Would storing the images directly in HBase be any better? >> >> On Thu, Jan 19, 2017 at 12:02 PM, Josh Mahonin >> wrote: >> >>> Hi Mark, >>> >>> At present, the Spark p
Re: Moving column family into new table
It's a bit peculiar that you've got it pre-split to 10 salt buckets, but seeing 400+ partitions. It sounds like HBase is splitting the regions on you, possibly due to the 'hbase.hregion.max.filesize' setting. You should be able to check the HBase Master UI and see the table details to see how many regions there are, and what nodes they're located on. Right now, the Phoenix MR / Spark integration basically assigns one partition per region. As a total guess, I wonder if somehow the first 380 partitions are relatively sparse, and the bulk of the data is in the remaining 70 partitions. You might be able to diagnose that by adding some logging in a 'mapPartitions()' call. It's possible that running a major compaction on that table might help redistribute the data as well. If you're seeing your task getting killed, definitely try dig into the Spark executor / driver logs to try find a root cause. If you're using YARN, you can usually get into the Spark history server, then check the 'stdout' / 'stderr' logs for each executor. Re: architecture recommendations, it's possible that phoenix-spark isn't the right tool for this job, though we routinely read / write billions of rows with it. I'd recommend trying to start with a smaller subset of your data and make sure you've got the schema, queries and HBase settings setup the way you like, then add Spark into the mix. Then start adding a bit more data, check results, find any bottlenecks, and tune as needed. If you're able to identify any issues specifically with Phoenix, bug reports and patches are greatly appreciated! Best of luck, Josh On Thu, Jan 19, 2017 at 12:30 PM, Mark Heppner wrote: > Thanks for the quick reply, Josh! > > For our demo cluster, we have 5 nodes, so the table was already set to 10 > salt buckets. I know you can increase the salt buckets after the table is > created, but how do you change the split points? The repartition in Spark > seemed to be extremely inefficient, so we were trying to skip it and keep > the 400+ default partitions. > > The biggest issue we're facing is that as Spark goes through the > partitions during the scan, it becomes exponentially slower towards the > end. Around task 380/450, it slows down to a halt, eventually timing out > around 410 and getting killed. We have no idea if this is something with > Spark, YARN, or HBase, so that's why we were brainstorming with using the > foreign key-based layout, hoping that the files on HDFS would be more > compacted. > > We haven't noticed too much network overhead, nor have we seen CPU or RAM > usage too high. Our nodes are pretty big, 32 cores and 256 GB RAM each, > connected on a 10 GbE network. Even if our query is for 80-100 rows, the > Spark job still slows to a crawl at the end, but that should really only be > about 80 MB of data it would be pulling out of Phoenix into the executors. > I guess we should have verified that the Phoenix+Spark plugin did achieve > data locality, but there isn't anything that says otherwise. Even though it > doesn't have data locality, we have no idea why it would progressively slow > down as it reaches the end of the scan/filter. > > The images are converted to a NumPy array, then saved as a binary string > into Phoenix. In Spark, this is fairly quick to convert the binary string > back to the NumPy array. This also allows us to use GET_BYTE() from Phoenix > to extract specific values within the array, without going through Spark at > all. Do you have any other architecture recommendations for our use case? > Would storing the images directly in HBase be any better? > > On Thu, Jan 19, 2017 at 12:02 PM, Josh Mahonin wrote: > >> Hi Mark, >> >> At present, the Spark partitions are basically equivalent to the number >> of regions in the underlying HBase table. This is typically something you >> can control yourself, either using pre-splitting or salting ( >> https://phoenix.apache.org/faq.html#Are_there_any_tips_for_ >> optimizing_Phoenix). Given that you have 450+ partitions though, it >> sounds like you should be able to achieve a decent level or parallelism, >> but that's a knob you can fiddle with. It might also be useful to look at >> Spark's "repartition" operation if you have idle Spark executors. >> >> The partitioning is sort of orthogonal from the primary key layout and >> the resulting query efficiency, but the strategy you've taken with your >> schema seems fairly sensible to me. Given that your primary key is the 'id' >> field, the query you're using is going to be much more efficient than, >> e.g., filtering on the 'title' column. Iterating on your schema and queries >> using straight SQL and then applying that to Spark after is probably a good >> strategy here to get more familiar with query performance. >> >> If you're reading the binary 'data' column in Spark and seeing a lot of >> network overhead, one thing to be aware of is the present Phoenix MR / >> Spark code isn't location aware, so executors are likely reading bi
Re: Moving column family into new table
Jonathan, I do check the queries using EXPLAIN, but it doesn't work the same in Spark. In Spark, I can only see a very generic plan and it only tells me if certain filters are pushed down to Phoenix or not. Query hints are ignored, since they're first translated by the Spark or Hive query evaluator, before ever getting sent to Phoenix. The design of the separate column families seems to work pretty great for us for Phoenix directly, but not so much for Spark. I can't figure out why Spark is slowing down so much at the end of the scan. On Thu, Jan 19, 2017 at 1:09 PM, Jonathan Leech wrote: > Do an explain on your query to confirm that it's doing a full scan and not > a skip scan. > > I typically use an in () clause instead of or, especially with compound > keys. I have also had to hint queries to use a skip scan, e.g /*+ SKIP_SCAN > */. > > Phoenix seems to do a very good job not reading data from column families > that aren't needed by the query, so I think your schema design is fine. > > On Jan 19, 2017, at 10:30 AM, Mark Heppner wrote: > > Thanks for the quick reply, Josh! > > For our demo cluster, we have 5 nodes, so the table was already set to 10 > salt buckets. I know you can increase the salt buckets after the table is > created, but how do you change the split points? The repartition in Spark > seemed to be extremely inefficient, so we were trying to skip it and keep > the 400+ default partitions. > > The biggest issue we're facing is that as Spark goes through the > partitions during the scan, it becomes exponentially slower towards the > end. Around task 380/450, it slows down to a halt, eventually timing out > around 410 and getting killed. We have no idea if this is something with > Spark, YARN, or HBase, so that's why we were brainstorming with using the > foreign key-based layout, hoping that the files on HDFS would be more > compacted. > > We haven't noticed too much network overhead, nor have we seen CPU or RAM > usage too high. Our nodes are pretty big, 32 cores and 256 GB RAM each, > connected on a 10 GbE network. Even if our query is for 80-100 rows, the > Spark job still slows to a crawl at the end, but that should really only be > about 80 MB of data it would be pulling out of Phoenix into the executors. > I guess we should have verified that the Phoenix+Spark plugin did achieve > data locality, but there isn't anything that says otherwise. Even though it > doesn't have data locality, we have no idea why it would progressively slow > down as it reaches the end of the scan/filter. > > The images are converted to a NumPy array, then saved as a binary string > into Phoenix. In Spark, this is fairly quick to convert the binary string > back to the NumPy array. This also allows us to use GET_BYTE() from Phoenix > to extract specific values within the array, without going through Spark at > all. Do you have any other architecture recommendations for our use case? > Would storing the images directly in HBase be any better? > > On Thu, Jan 19, 2017 at 12:02 PM, Josh Mahonin wrote: > >> Hi Mark, >> >> At present, the Spark partitions are basically equivalent to the number >> of regions in the underlying HBase table. This is typically something you >> can control yourself, either using pre-splitting or salting ( >> https://phoenix.apache.org/faq.html#Are_there_any_tips_for_ >> optimizing_Phoenix). Given that you have 450+ partitions though, it >> sounds like you should be able to achieve a decent level or parallelism, >> but that's a knob you can fiddle with. It might also be useful to look at >> Spark's "repartition" operation if you have idle Spark executors. >> >> The partitioning is sort of orthogonal from the primary key layout and >> the resulting query efficiency, but the strategy you've taken with your >> schema seems fairly sensible to me. Given that your primary key is the 'id' >> field, the query you're using is going to be much more efficient than, >> e.g., filtering on the 'title' column. Iterating on your schema and queries >> using straight SQL and then applying that to Spark after is probably a good >> strategy here to get more familiar with query performance. >> >> If you're reading the binary 'data' column in Spark and seeing a lot of >> network overhead, one thing to be aware of is the present Phoenix MR / >> Spark code isn't location aware, so executors are likely reading big chunks >> of data from another node. There's a few patches in to address this, but >> they're not in a released version yet: >> >> https://issues.apache.org/jira/browse/PHOENIX-3600 >> https://issues.apache.org/jira/browse/PHOENIX-3601 >> >> Good luck! >> >> Josh >> >> >> >> >> On Thu, Jan 19, 2017 at 11:30 AM, Mark Heppner >> wrote: >> >>> Our use case is to analyze images using Spark. The images are typically >>> ~1MB each, so in order to prevent the small files problem in HDFS, we went >>> with HBase and Phoenix. For 20+ million images and metadata, this has been >>> working pretty well so far.
Re: Moving column family into new table
Do an explain on your query to confirm that it's doing a full scan and not a skip scan. I typically use an in () clause instead of or, especially with compound keys. I have also had to hint queries to use a skip scan, e.g /*+ SKIP_SCAN */. Phoenix seems to do a very good job not reading data from column families that aren't needed by the query, so I think your schema design is fine. > On Jan 19, 2017, at 10:30 AM, Mark Heppner wrote: > > Thanks for the quick reply, Josh! > > For our demo cluster, we have 5 nodes, so the table was already set to 10 > salt buckets. I know you can increase the salt buckets after the table is > created, but how do you change the split points? The repartition in Spark > seemed to be extremely inefficient, so we were trying to skip it and keep the > 400+ default partitions. > > The biggest issue we're facing is that as Spark goes through the partitions > during the scan, it becomes exponentially slower towards the end. Around task > 380/450, it slows down to a halt, eventually timing out around 410 and > getting killed. We have no idea if this is something with Spark, YARN, or > HBase, so that's why we were brainstorming with using the foreign key-based > layout, hoping that the files on HDFS would be more compacted. > > We haven't noticed too much network overhead, nor have we seen CPU or RAM > usage too high. Our nodes are pretty big, 32 cores and 256 GB RAM each, > connected on a 10 GbE network. Even if our query is for 80-100 rows, the > Spark job still slows to a crawl at the end, but that should really only be > about 80 MB of data it would be pulling out of Phoenix into the executors. I > guess we should have verified that the Phoenix+Spark plugin did achieve data > locality, but there isn't anything that says otherwise. Even though it > doesn't have data locality, we have no idea why it would progressively slow > down as it reaches the end of the scan/filter. > > The images are converted to a NumPy array, then saved as a binary string into > Phoenix. In Spark, this is fairly quick to convert the binary string back to > the NumPy array. This also allows us to use GET_BYTE() from Phoenix to > extract specific values within the array, without going through Spark at all. > Do you have any other architecture recommendations for our use case? Would > storing the images directly in HBase be any better? > >> On Thu, Jan 19, 2017 at 12:02 PM, Josh Mahonin wrote: >> Hi Mark, >> >> At present, the Spark partitions are basically equivalent to the number of >> regions in the underlying HBase table. This is typically something you can >> control yourself, either using pre-splitting or salting >> (https://phoenix.apache.org/faq.html#Are_there_any_tips_for_optimizing_Phoenix). >> Given that you have 450+ partitions though, it sounds like you should be >> able to achieve a decent level or parallelism, but that's a knob you can >> fiddle with. It might also be useful to look at Spark's "repartition" >> operation if you have idle Spark executors. >> >> The partitioning is sort of orthogonal from the primary key layout and the >> resulting query efficiency, but the strategy you've taken with your schema >> seems fairly sensible to me. Given that your primary key is the 'id' field, >> the query you're using is going to be much more efficient than, e.g., >> filtering on the 'title' column. Iterating on your schema and queries using >> straight SQL and then applying that to Spark after is probably a good >> strategy here to get more familiar with query performance. >> >> If you're reading the binary 'data' column in Spark and seeing a lot of >> network overhead, one thing to be aware of is the present Phoenix MR / Spark >> code isn't location aware, so executors are likely reading big chunks of >> data from another node. There's a few patches in to address this, but >> they're not in a released version yet: >> >> https://issues.apache.org/jira/browse/PHOENIX-3600 >> https://issues.apache.org/jira/browse/PHOENIX-3601 >> >> Good luck! >> >> Josh >> >> >> >> >>> On Thu, Jan 19, 2017 at 11:30 AM, Mark Heppner >>> wrote: >>> Our use case is to analyze images using Spark. The images are typically >>> ~1MB each, so in order to prevent the small files problem in HDFS, we went >>> with HBase and Phoenix. For 20+ million images and metadata, this has been >>> working pretty well so far. Since this is pretty new to us, we didn't >>> create a robust design: >>> >>> CREATE TABLE IF NOT EXISTS mytable >>> ( >>> id VARCHAR(36) NOT NULL PRIMARY KEY, >>> title VARCHAR, >>> ... >>> image.dtype VARCHAR(12), >>> image.width UNSIGNED_INT, >>> image.height UNSIGNED_INT, >>> image.data VARBINARY >>> ) >>> >>> Most queries are on the metadata, so all of that is kept in the default >>> column family. Only the image data is stored in a secondary column family. >>> Additional indexes are created anyways, so the main tabl
Re: Phoenix tracing did not start
Pradheep, I don't think this works in HDP 2.5 either, I've never been able to get it to work. On Thu, Jan 19, 2017 at 4:29 AM, Ankit Singhal wrote: > Hi Pradheep, > > It seems tracing is not distributed as a part of HDP 2.4.3.0, please work > with your vendor for an appropriate solution. > > Regards, > Ankit Singhal > > On Thu, Jan 19, 2017 at 4:48 AM, Pradheep Shanmugam < > pradheep.shanmu...@infor.com> wrote: > >> Hi, >> >> I am using hdp 2.4.3.0-227. I am trying to enable phoenix tracing to >> monitor the queries and to analyze performance. I followed steps outlined >> here - https://phoenix.apache.org/tracing.html >> i placed the hadoop-metrics2-hbase.properties in /etc/hbase/conf >> hadoop-metrics2-phoenix.properties in /usr/hdp/2.4.3.0-227/phoenix/bin >> in all regions servers >> >> Aslo added following properties to hbase.site >> phoenix.trace.statsTableName SYSTEM.TRACING_STATS> alue> >> phoenix.trace.frequency always. After this >> >> I am not clear where to place the ddl for SYSTEM.TRACING_STATS. Also i >> could not see ./bin/traceserver.py to start >> Please advice. >> >> Thanks, >> Pradheep >> > > -- Mark Heppner
Re: Moving column family into new table
Thanks for the quick reply, Josh! For our demo cluster, we have 5 nodes, so the table was already set to 10 salt buckets. I know you can increase the salt buckets after the table is created, but how do you change the split points? The repartition in Spark seemed to be extremely inefficient, so we were trying to skip it and keep the 400+ default partitions. The biggest issue we're facing is that as Spark goes through the partitions during the scan, it becomes exponentially slower towards the end. Around task 380/450, it slows down to a halt, eventually timing out around 410 and getting killed. We have no idea if this is something with Spark, YARN, or HBase, so that's why we were brainstorming with using the foreign key-based layout, hoping that the files on HDFS would be more compacted. We haven't noticed too much network overhead, nor have we seen CPU or RAM usage too high. Our nodes are pretty big, 32 cores and 256 GB RAM each, connected on a 10 GbE network. Even if our query is for 80-100 rows, the Spark job still slows to a crawl at the end, but that should really only be about 80 MB of data it would be pulling out of Phoenix into the executors. I guess we should have verified that the Phoenix+Spark plugin did achieve data locality, but there isn't anything that says otherwise. Even though it doesn't have data locality, we have no idea why it would progressively slow down as it reaches the end of the scan/filter. The images are converted to a NumPy array, then saved as a binary string into Phoenix. In Spark, this is fairly quick to convert the binary string back to the NumPy array. This also allows us to use GET_BYTE() from Phoenix to extract specific values within the array, without going through Spark at all. Do you have any other architecture recommendations for our use case? Would storing the images directly in HBase be any better? On Thu, Jan 19, 2017 at 12:02 PM, Josh Mahonin wrote: > Hi Mark, > > At present, the Spark partitions are basically equivalent to the number of > regions in the underlying HBase table. This is typically something you can > control yourself, either using pre-splitting or salting ( > https://phoenix.apache.org/faq.html#Are_there_any_tips_ > for_optimizing_Phoenix). Given that you have 450+ partitions though, it > sounds like you should be able to achieve a decent level or parallelism, > but that's a knob you can fiddle with. It might also be useful to look at > Spark's "repartition" operation if you have idle Spark executors. > > The partitioning is sort of orthogonal from the primary key layout and the > resulting query efficiency, but the strategy you've taken with your schema > seems fairly sensible to me. Given that your primary key is the 'id' field, > the query you're using is going to be much more efficient than, e.g., > filtering on the 'title' column. Iterating on your schema and queries using > straight SQL and then applying that to Spark after is probably a good > strategy here to get more familiar with query performance. > > If you're reading the binary 'data' column in Spark and seeing a lot of > network overhead, one thing to be aware of is the present Phoenix MR / > Spark code isn't location aware, so executors are likely reading big chunks > of data from another node. There's a few patches in to address this, but > they're not in a released version yet: > > https://issues.apache.org/jira/browse/PHOENIX-3600 > https://issues.apache.org/jira/browse/PHOENIX-3601 > > Good luck! > > Josh > > > > > On Thu, Jan 19, 2017 at 11:30 AM, Mark Heppner > wrote: > >> Our use case is to analyze images using Spark. The images are typically >> ~1MB each, so in order to prevent the small files problem in HDFS, we went >> with HBase and Phoenix. For 20+ million images and metadata, this has been >> working pretty well so far. Since this is pretty new to us, we didn't >> create a robust design: >> >> CREATE TABLE IF NOT EXISTS mytable >> ( >> id VARCHAR(36) NOT NULL PRIMARY KEY, >> title VARCHAR, >> ... >> image.dtype VARCHAR(12), >> image.width UNSIGNED_INT, >> image.height UNSIGNED_INT, >> image.data VARBINARY >> ) >> >> Most queries are on the metadata, so all of that is kept in the default >> column family. Only the image data is stored in a secondary column family. >> Additional indexes are created anyways, so the main table isn't usually >> touched. >> >> We first run a Phoenix query to check if there are any matches. If so, >> then we start a Spark job on the images. The primary keys are sent to the >> PySpark job, which then grabs the images based on the primary keys: >> >> df = sqlContext.read \ >> .format('org.apache.phoenix.spark') \ >> .option('table', 'mytable') \ >> .option('zkUrl', 'localhost:2181:/hbase-unsecure') \ >> .load() >> df.registerTempTable('mytable') >> >> query = >> df_imgs = sqlContext.sql( >> 'SELECT IMAGE FROM mytable WHERE ID = 1 OR ID = 2 ...' >> ) >> >> When this was first designed, we thou
Re: Moving column family into new table
Hi Mark, At present, the Spark partitions are basically equivalent to the number of regions in the underlying HBase table. This is typically something you can control yourself, either using pre-splitting or salting ( https://phoenix.apache.org/faq.html#Are_there_any_tips_for_optimizing_Phoenix). Given that you have 450+ partitions though, it sounds like you should be able to achieve a decent level or parallelism, but that's a knob you can fiddle with. It might also be useful to look at Spark's "repartition" operation if you have idle Spark executors. The partitioning is sort of orthogonal from the primary key layout and the resulting query efficiency, but the strategy you've taken with your schema seems fairly sensible to me. Given that your primary key is the 'id' field, the query you're using is going to be much more efficient than, e.g., filtering on the 'title' column. Iterating on your schema and queries using straight SQL and then applying that to Spark after is probably a good strategy here to get more familiar with query performance. If you're reading the binary 'data' column in Spark and seeing a lot of network overhead, one thing to be aware of is the present Phoenix MR / Spark code isn't location aware, so executors are likely reading big chunks of data from another node. There's a few patches in to address this, but they're not in a released version yet: https://issues.apache.org/jira/browse/PHOENIX-3600 https://issues.apache.org/jira/browse/PHOENIX-3601 Good luck! Josh On Thu, Jan 19, 2017 at 11:30 AM, Mark Heppner wrote: > Our use case is to analyze images using Spark. The images are typically > ~1MB each, so in order to prevent the small files problem in HDFS, we went > with HBase and Phoenix. For 20+ million images and metadata, this has been > working pretty well so far. Since this is pretty new to us, we didn't > create a robust design: > > CREATE TABLE IF NOT EXISTS mytable > ( > id VARCHAR(36) NOT NULL PRIMARY KEY, > title VARCHAR, > ... > image.dtype VARCHAR(12), > image.width UNSIGNED_INT, > image.height UNSIGNED_INT, > image.data VARBINARY > ) > > Most queries are on the metadata, so all of that is kept in the default > column family. Only the image data is stored in a secondary column family. > Additional indexes are created anyways, so the main table isn't usually > touched. > > We first run a Phoenix query to check if there are any matches. If so, > then we start a Spark job on the images. The primary keys are sent to the > PySpark job, which then grabs the images based on the primary keys: > > df = sqlContext.read \ > .format('org.apache.phoenix.spark') \ > .option('table', 'mytable') \ > .option('zkUrl', 'localhost:2181:/hbase-unsecure') \ > .load() > df.registerTempTable('mytable') > > query = > df_imgs = sqlContext.sql( > 'SELECT IMAGE FROM mytable WHERE ID = 1 OR ID = 2 ...' > ) > > When this was first designed, we thought since the lookup was by primary > key, it would be smart enough to do a skip scan, but it appears to be doing > a full scan. The df_imgs.rdd.getNumPartitions() ends up being 450+, which > matches up with the number of split files in HDFS. > > Would it be better to use a foreign key and split the tables : > > CREATE TABLE IF NOT EXISTS mytable > ( > id VARCHAR(36) NOT NULL PRIMARY KEY, > title VARCHAR, > image_id VARCHAR(36) > ) > CREATE TABLE IF NOT EXISTS images > ( > image_id VARCHAR(36) NOT NULL PRIMARY KEY, > dtype VARCHAR(12), > width UNSIGNED_INT, > height UNSIGNED_INT, > data VARBINARY > ) > > If the first query grabs the image_ids and send them to Spark, would Spark > be able to handle the query more efficiently? > > If this is a better design, is there any way of moving the "image" column > family from "mytable" to the default column family of the new "images" > table? Is it possible to create the new table with the "image_id"s, make > the foreign keys, then move the column family into the new table? > > > -- > Mark Heppner >
Moving column family into new table
Our use case is to analyze images using Spark. The images are typically ~1MB each, so in order to prevent the small files problem in HDFS, we went with HBase and Phoenix. For 20+ million images and metadata, this has been working pretty well so far. Since this is pretty new to us, we didn't create a robust design: CREATE TABLE IF NOT EXISTS mytable ( id VARCHAR(36) NOT NULL PRIMARY KEY, title VARCHAR, ... image.dtype VARCHAR(12), image.width UNSIGNED_INT, image.height UNSIGNED_INT, image.data VARBINARY ) Most queries are on the metadata, so all of that is kept in the default column family. Only the image data is stored in a secondary column family. Additional indexes are created anyways, so the main table isn't usually touched. We first run a Phoenix query to check if there are any matches. If so, then we start a Spark job on the images. The primary keys are sent to the PySpark job, which then grabs the images based on the primary keys: df = sqlContext.read \ .format('org.apache.phoenix.spark') \ .option('table', 'mytable') \ .option('zkUrl', 'localhost:2181:/hbase-unsecure') \ .load() df.registerTempTable('mytable') query = df_imgs = sqlContext.sql( 'SELECT IMAGE FROM mytable WHERE ID = 1 OR ID = 2 ...' ) When this was first designed, we thought since the lookup was by primary key, it would be smart enough to do a skip scan, but it appears to be doing a full scan. The df_imgs.rdd.getNumPartitions() ends up being 450+, which matches up with the number of split files in HDFS. Would it be better to use a foreign key and split the tables : CREATE TABLE IF NOT EXISTS mytable ( id VARCHAR(36) NOT NULL PRIMARY KEY, title VARCHAR, image_id VARCHAR(36) ) CREATE TABLE IF NOT EXISTS images ( image_id VARCHAR(36) NOT NULL PRIMARY KEY, dtype VARCHAR(12), width UNSIGNED_INT, height UNSIGNED_INT, data VARBINARY ) If the first query grabs the image_ids and send them to Spark, would Spark be able to handle the query more efficiently? If this is a better design, is there any way of moving the "image" column family from "mytable" to the default column family of the new "images" table? Is it possible to create the new table with the "image_id"s, make the foreign keys, then move the column family into the new table? -- Mark Heppner
Re: Phoenix tracing did not start
Hi Pradheep, It seems tracing is not distributed as a part of HDP 2.4.3.0, please work with your vendor for an appropriate solution. Regards, Ankit Singhal On Thu, Jan 19, 2017 at 4:48 AM, Pradheep Shanmugam < pradheep.shanmu...@infor.com> wrote: > Hi, > > I am using hdp 2.4.3.0-227. I am trying to enable phoenix tracing to > monitor the queries and to analyze performance. I followed steps outlined > here - https://phoenix.apache.org/tracing.html > i placed the hadoop-metrics2-hbase.properties in /etc/hbase/conf > hadoop-metrics2-phoenix.properties in /usr/hdp/2.4.3.0-227/phoenix/bin in > all regions servers > > Aslo added following properties to hbase.site > phoenix.trace.statsTableName SYSTEM.TRACING_STATS value> > phoenix.trace.frequency always. After this > > I am not clear where to place the ddl for SYSTEM.TRACING_STATS. Also i > could not see ./bin/traceserver.py to start > Please advice. > > Thanks, > Pradheep >